TY - JOUR
AU - , de Lange, Elvira S
AB - Abstract Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers. biological control, integrated pest management, precision agriculture, remote sensing, unmanned aerial system Arthropod pest outbreaks in field crops and orchards often show nonuniform spatial distributions. For some pests, such as cabbage aphids [Brevicoryne brassicae L. (Hemiptera: Aphididae)] in canola fields (Brassica spp.), and Asian citrus psyllids [Diaphorina citri Kuwayama (Hemiptera: Liviidae)] in citrus orchards (Citrus spp.) there is evidence of highest population densities along field edges (Sétamou and Bartels 2015, Severtson et al. 2015, Nguyen and Nansen 2018). For other pests, such as soybean aphids [Aphis glycines Matsumura (Hemiptera: Aphididae)] in soybean (Glycine max (L.) Merrill), and two-spotted spider mites [Tetranychus urticae Koch (Acari: Tetranychidae)] in cowpea (Vigna unguiculata (L.) Walp.), parts of fields that are exposed to abiotic stress, such as drought or nutrient deficiencies, tend to be more susceptible (Mattson and Haack 1987, Abdel-Galil et al. 2007, Walter and DiFonzo 2007, Amtmann et al. 2008, West and Nansen 2014). Thus, as pests are spatially aggregated, precision agriculture technologies can offer important opportunities for integrated pest management (IPM) (Lillesand et al. 2007). Precision pest management is twofold: first, reflectance-based crop monitoring (using ground-based, airborne, or orbital remote sensing technologies) can be used to identify pest hotspots. Second, precision control systems, such as distributors of natural enemies and pesticide spray rigs, can provide localized solutions. Both technologies can be mounted on equipment moving through fields (such as irrigation equipment), on manned or unmanned vehicles driving around in fields, or on aerial drones. In this review, we focus specifically on the use of small drones in IPM. Small drones are here defined as remotely controlled, unmanned flying robots that weigh more than 250 g but less than 25 kg, including payload (FAA 2018a). These types of drones typically have flight-times of a few minutes to hours and limited ranges (Hardin and Jensen 2011). We will also briefly discuss the larger drones that are typically used for pesticide sprays. Discussion of smaller and larger drones is beyond the scope of this review, but see Watts et al. (2012), and Anderson and Gaston (2013) for more information. Drones used for detection of pest hotspots are here referred to as sensing drones, while drones used for precision distribution of solutions are referred to as actuation drones. Both types of drones could communicate to establish a closed-loop IPM solution (Fig. 1). Importantly, use of drones in precision pest management could be cost-effective and reduce harm to the environment. Sensing drones could reduce the time required to scout for pests, while actuation drones could reduce the area where pesticide applications are necessary, and reduce the costs of dispensing natural enemies. Fig. 1. Open in new tabDownload slide (a) State-of-the-art open-loop remote sensing paradigm and (b) closed-loop IPM paradigm envisioned in this article. Sensing drones could be used for detection of pest hotspots, while actuation drones could be used for precision distribution of solutions. Adapted from Teske et al. (2019). Fig. 1. Open in new tabDownload slide (a) State-of-the-art open-loop remote sensing paradigm and (b) closed-loop IPM paradigm envisioned in this article. Sensing drones could be used for detection of pest hotspots, while actuation drones could be used for precision distribution of solutions. Adapted from Teske et al. (2019). Reports of drones in agriculture started appearing around 1998 and increased dramatically in the last decade (Fig. 2). According to the abstract of a licensed report, the worldwide drone market value is currently estimated about $6.8 billion and is anticipated to reach $36.9 billion by 2022 (WinterGreen Research 2016b). Another paid report predicts that drones will reach a value of $14.3 billion by 2028 (Teal Group 2019). Agricultural small drones currently account for about $500 million, and their value is expected to reach $3.7 billion by 2022 (WinterGreen Research 2016a). A different paid report predicts similar values (ABI Research 2018), while a freely available resource predicts the value of drone-based solutions for agriculture at $32 billion (PwC 2016). Recently, the United Nations published a report on the use of drones for agriculture, stressing its potential benefits for food security (Sylvester 2018). A text message poll among ca. 900 growers based in the United States showed that around 30% use drone-based technology for farming practices (Farm Journal Pulse 2019). Thus, although there is a big margin among predictions of future drone use, an increasing number of growers is expected to use and/or own a drone within the next decade. Fig. 2. Open in new tabDownload slide Number of articles published between 1998 and 2018 on the use of drones in agriculture. Shown is the number of publications for each year mentioning ‘drone’, ‘UAV’ (Unmanned Aerial Vehicle), or ‘UAS’ (Unmanned Aerial System) and ‘agriculture’. The words ‘bee’, ‘honey bee’, and ‘hive’ were explicitly excluded from the search, to avoid including publications on drones defined as male bees. Source: Web of Science. Fig. 2. Open in new tabDownload slide Number of articles published between 1998 and 2018 on the use of drones in agriculture. Shown is the number of publications for each year mentioning ‘drone’, ‘UAV’ (Unmanned Aerial Vehicle), or ‘UAS’ (Unmanned Aerial System) and ‘agriculture’. The words ‘bee’, ‘honey bee’, and ‘hive’ were explicitly excluded from the search, to avoid including publications on drones defined as male bees. Source: Web of Science. There are various ways to classify drones (Watts et al. 2012). For our purpose, we currently distinguish two major types of small drones: rotary wing and fixed wing. Each of these has its own advantages and limitations (Hogan et al. 2017). Multi-rotor and single-rotor (helicopter) drones do not require specific structures for take-off and landing. Moreover, they can hover and perform agile maneuvering, making them suitable for applications (e.g., inspection of crops and orchards or pesticide applications) where precise maneuvering or the ability to maintain a visual of a target for an extended period of time is required. Especially multi-rotor drones tend to be easy to use, and relatively cheap to obtain. Fixed-wing systems are usually faster than rotor-based systems, and generally larger in size, allowing for higher payloads (Stark et al. 2013b, Dalamagkidis 2015). Both have been used for precision agriculture (Barbedo 2019). Since drone technology quickly improves, we will refrain from discussing drone types in further detail, but see Dalamagkidis (2015) and Stark et al. (2013b) for more information. A number of reviews discuss the use of drones in precision agriculture, focusing on airborne remote sensing for various applications, such as predicting yield and characterizing soil properties (Hardin and Jensen 2011, Prabhakar et al. 2012, Zhang and Kovacs 2012, Mulla 2013, Gago et al. 2015, Nansen and Elliott 2016, Pádua et al. 2017, Hunt and Daughtry 2018, Aasen et al. 2018, Gonzalez et al. 2018, Barbedo 2019, Maes and Steppe 2019). In this review, we focus on precision management of arthropod pests and describe the use of both sensing and actuation drones. First, we provide an update about airborne remote sensing-based detection of pest problems. Then, we evaluate the possibilities of actuation drones for precision distribution of pesticides and natural enemies. Also, we discuss the possibilities of sensing and actuation drones for novel functions in pest management. Lastly, we discuss challenges and opportunities in the adoption of drone technology in modern agriculture. Sensing Drones to Monitor Crop Health Traditional field scouting for pest infestations is often expensive and time-consuming (Hodgson et al. 2004, Severtson et al. 2016b, Dara 2019). It may be practically challenging, such as when a large acreage is involved, when the arthropod pests are too small to see with the naked eye, or when they reside in the soil or in tall trees. In some cropping systems, effective scouting is hampered by lack of reliable pest sampling techniques. Hence, one of the main drivers for the implementation of drone-based remote sensing technologies into agriculture is the potential time saved by automatizing crop monitoring, making the technology cost-effective for growers (Carrière et al. 2006, Backoulou et al. 2011a, Dara 2019). Compared to conventional platforms for remote sensing, such as ground-based, aerial (with manned aircraft) and orbital (with satellites such as Landsat [30 m spatial resolution], Sentinel 2 [10 m] or RapidEye [5 m]; Mulla 2013), sensing drones present several advantages that make them attractive for use in precision agriculture. Sensing drones potentially allow for coverage of larger areas than ground-based, handheld devices. They can fly at lower altitudes than manned aircraft and orbital systems, increasing images’ spatial resolution and reducing the number of mixed pixels (pixels representing reflectance of both plant and soil, discussed in more detail below). Also, they cost less to obtain and deploy than manned aircraft and satellites and do not have long revisiting times like satellites, allowing for higher monitoring frequencies (Zhang and Kovacs 2012, Mulla 2013, Matese et al. 2015, Aasen and Bolten 2018, Barbedo 2019, Maes and Steppe 2019). Remote Sensing in Precision Agriculture Remote sensing is the detection of energy emitted or reflected by various objects, either in the form of acoustical energy or in the form of electromagnetic energy (including ultraviolet [UV] light, visible light, and infrared light) (Usha and Singh 2013). It is a non-invasive, relatively labor-extensive method that could be used to detect plant stress before changes are visible by eye. For crops, remote sensing equipment generally assesses the spectral range of visible light or photosynthetically active radiation (PAR, 400–700 nm) and near-infrared light (NIR, 700–1,400 nm), with most studies referring to the 400–1,000 nm range (Nansen 2016). Particular stressors, such as arthropod infestations, induce physiological plant responses, causing changes in the plants’ ability to perform photosynthesis, which leads to changes in leaf reflectance in parts of this spectral range. For aerial remote sensing, a drone can be equipped with an RGB (red green blue) sensor, a multispectral sensor with between 3 and 12 broad spectral bands, or a hyperspectral sensor with hundreds of narrow spectral bands. An RGB sensor is low-cost, but results in limited spectral information. A multispectral sensor results in more spectral information, but a hyperspectral sensor is generally much better at differentiating subtle differences in canopy reflectance than a multispectral sensor (Yang et al. 2009a). However, since hyperspectral sensors are generally larger, they would require mounting on drones adapted for heavier payloads. Also, they are generally more expensive, and data analysis requires more time and experience, limiting use for individual growers. A comprehensive review of the sensor types compatible with drones has been written by Aasen et al. (2018). Remote Sensing and Arthropod Pests Remote sensing technologies have been used in precision agriculture for the last few decades, with various applications, such as yield predictions and evaluation of crop phenology (Mulla 2013). Also, these techniques are being used to monitor different abiotic plant stressors, such as drought (Gago et al. 2015, Katsoulas et al. 2016, Zhao et al. 2017, Jorge et al. 2019) and nutritional deficiencies (Quemada et al. 2014), and biotic plant stressors, such as pathogens (Calderón et al. 2013, Mahlein et al. 2013, Zarco-Tejada et al. 2018), nematodes (Nutter et al. 2002), and weeds (Rasmussen et al. 2013, Peña et al. 2015). Likewise, remote sensing technologies have been successfully used to detect stress caused by various arthropod pests on a wide variety of field and orchard crops (Riley 1989, Nansen 2016, Nansen and Elliott 2016; Tables 1–4). A limited amount of studies concerning arthropod-induced stress detection used drone-based aerial remote sensing (Table 1), manned aircraft-based aerial remote sensing (Table 2), or orbital remote sensing (Table 3), while most studies used ground-based remote sensing (Table 4). Table 1. Studies on drone-based hyperspectral, multispectral, and RGB remote sensing to detect arthropod-induced stress in crops and orchards Platform details . Type . Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . md4-1000, Microdrones Four rotors RGB α ILCE-5100L with an E 20 mm F2.8 lens, Sony 3 Visual inspection of images Grape Vitis vinifera L. Cotton jassid Jacobiasca lybica Bergevin and Zanon Hemiptera: Cicadellidae Del-Campo- Sanchez et al. 2019 Aeryon Scout, Aeryon Labs Inc. Four rotors RGB + M Photo3S, Aeryon Labs Inc. + ADC-Lite, Tetracam Inc. 3 + 3 Outbreak reported by grower Wheat Triticum aestivum Fall armyworm Spodoptera frugiperda Smith Lepidoptera: Noctuidae Zhang et al. 2014 S800 EVO, SZ DJI Technology Co. Six rotors RGB + M + H 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. 3 + 5 + 274 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Fitch Hemiptera: Phylloxeridae Vanegas et al. 2018a S800 EVO, SZ DJI Technology Co. / Phantom3 Pro, SZ DJI Technology Co. Six rotors / four rotors RGB + M + H / RGB 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. / Phantom3 Pro associated camerab 3 + 5 + 274 / 3 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Vanegas et al. 2018b eBee, senseFly Fixed wing M S110 NIRb, Canon 3 NA Onion Allium cepa L. Thrips NA Thysanoptera: Thripidae Nebiker et al. 2016 Cinestar-8 MK Heavy Lift, Freefly Systems Eight rotors M Mini-MCA6, Tetracam Inc. 6 Arthropod counts, soil and plant tissue nutrient analysesc Canola Brassica spp. Green peach aphid Myzus persicae Hemiptera: Aphididae Severtson et al. 2016a Matrice 100, SZ DJI Technology Co. Four rotors M ADC-Lite, Tetracam Inc. 3 Damage assessments Cotton Gossypium hirsutum L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Huang et al. 2018 Spreading Wings S800, SZ DJI Technology Co. Six rotors M Mini-MCA6, Tetracam Inc. 6 Damage assessments Potato Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata Coleoptera: Chrysomelidae Hunt and Rondon 2017, Hunt et al. 2017 eBee, senseFly Fixed wing M S110 NIRb, Canon 3 Arthropod counts Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Stanton et al. 2017 Platform details . Type . Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . md4-1000, Microdrones Four rotors RGB α ILCE-5100L with an E 20 mm F2.8 lens, Sony 3 Visual inspection of images Grape Vitis vinifera L. Cotton jassid Jacobiasca lybica Bergevin and Zanon Hemiptera: Cicadellidae Del-Campo- Sanchez et al. 2019 Aeryon Scout, Aeryon Labs Inc. Four rotors RGB + M Photo3S, Aeryon Labs Inc. + ADC-Lite, Tetracam Inc. 3 + 3 Outbreak reported by grower Wheat Triticum aestivum Fall armyworm Spodoptera frugiperda Smith Lepidoptera: Noctuidae Zhang et al. 2014 S800 EVO, SZ DJI Technology Co. Six rotors RGB + M + H 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. 3 + 5 + 274 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Fitch Hemiptera: Phylloxeridae Vanegas et al. 2018a S800 EVO, SZ DJI Technology Co. / Phantom3 Pro, SZ DJI Technology Co. Six rotors / four rotors RGB + M + H / RGB 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. / Phantom3 Pro associated camerab 3 + 5 + 274 / 3 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Vanegas et al. 2018b eBee, senseFly Fixed wing M S110 NIRb, Canon 3 NA Onion Allium cepa L. Thrips NA Thysanoptera: Thripidae Nebiker et al. 2016 Cinestar-8 MK Heavy Lift, Freefly Systems Eight rotors M Mini-MCA6, Tetracam Inc. 6 Arthropod counts, soil and plant tissue nutrient analysesc Canola Brassica spp. Green peach aphid Myzus persicae Hemiptera: Aphididae Severtson et al. 2016a Matrice 100, SZ DJI Technology Co. Four rotors M ADC-Lite, Tetracam Inc. 3 Damage assessments Cotton Gossypium hirsutum L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Huang et al. 2018 Spreading Wings S800, SZ DJI Technology Co. Six rotors M Mini-MCA6, Tetracam Inc. 6 Damage assessments Potato Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata Coleoptera: Chrysomelidae Hunt and Rondon 2017, Hunt et al. 2017 eBee, senseFly Fixed wing M S110 NIRb, Canon 3 Arthropod counts Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Stanton et al. 2017 aRGB = red green blue, M = multispectral, H = hyperspectral. bNIR = near infrared. cRemote sensing was used to detect nutrient deficiencies, which were correlated to arthropod presence. NA = information not provided. Open in new tab Table 1. Studies on drone-based hyperspectral, multispectral, and RGB remote sensing to detect arthropod-induced stress in crops and orchards Platform details . Type . Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . md4-1000, Microdrones Four rotors RGB α ILCE-5100L with an E 20 mm F2.8 lens, Sony 3 Visual inspection of images Grape Vitis vinifera L. Cotton jassid Jacobiasca lybica Bergevin and Zanon Hemiptera: Cicadellidae Del-Campo- Sanchez et al. 2019 Aeryon Scout, Aeryon Labs Inc. Four rotors RGB + M Photo3S, Aeryon Labs Inc. + ADC-Lite, Tetracam Inc. 3 + 3 Outbreak reported by grower Wheat Triticum aestivum Fall armyworm Spodoptera frugiperda Smith Lepidoptera: Noctuidae Zhang et al. 2014 S800 EVO, SZ DJI Technology Co. Six rotors RGB + M + H 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. 3 + 5 + 274 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Fitch Hemiptera: Phylloxeridae Vanegas et al. 2018a S800 EVO, SZ DJI Technology Co. / Phantom3 Pro, SZ DJI Technology Co. Six rotors / four rotors RGB + M + H / RGB 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. / Phantom3 Pro associated camerab 3 + 5 + 274 / 3 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Vanegas et al. 2018b eBee, senseFly Fixed wing M S110 NIRb, Canon 3 NA Onion Allium cepa L. Thrips NA Thysanoptera: Thripidae Nebiker et al. 2016 Cinestar-8 MK Heavy Lift, Freefly Systems Eight rotors M Mini-MCA6, Tetracam Inc. 6 Arthropod counts, soil and plant tissue nutrient analysesc Canola Brassica spp. Green peach aphid Myzus persicae Hemiptera: Aphididae Severtson et al. 2016a Matrice 100, SZ DJI Technology Co. Four rotors M ADC-Lite, Tetracam Inc. 3 Damage assessments Cotton Gossypium hirsutum L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Huang et al. 2018 Spreading Wings S800, SZ DJI Technology Co. Six rotors M Mini-MCA6, Tetracam Inc. 6 Damage assessments Potato Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata Coleoptera: Chrysomelidae Hunt and Rondon 2017, Hunt et al. 2017 eBee, senseFly Fixed wing M S110 NIRb, Canon 3 Arthropod counts Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Stanton et al. 2017 Platform details . Type . Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . md4-1000, Microdrones Four rotors RGB α ILCE-5100L with an E 20 mm F2.8 lens, Sony 3 Visual inspection of images Grape Vitis vinifera L. Cotton jassid Jacobiasca lybica Bergevin and Zanon Hemiptera: Cicadellidae Del-Campo- Sanchez et al. 2019 Aeryon Scout, Aeryon Labs Inc. Four rotors RGB + M Photo3S, Aeryon Labs Inc. + ADC-Lite, Tetracam Inc. 3 + 3 Outbreak reported by grower Wheat Triticum aestivum Fall armyworm Spodoptera frugiperda Smith Lepidoptera: Noctuidae Zhang et al. 2014 S800 EVO, SZ DJI Technology Co. Six rotors RGB + M + H 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. 3 + 5 + 274 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Fitch Hemiptera: Phylloxeridae Vanegas et al. 2018a S800 EVO, SZ DJI Technology Co. / Phantom3 Pro, SZ DJI Technology Co. Six rotors / four rotors RGB + M + H / RGB 5DsR, Canon Inc. + RedEdge, MicaSense Inc. + Nano- Hyperspec, Headwall Photonics Inc. / Phantom3 Pro associated camerab 3 + 5 + 274 / 3 Ground traps and root digging, visual vigor assessments Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Vanegas et al. 2018b eBee, senseFly Fixed wing M S110 NIRb, Canon 3 NA Onion Allium cepa L. Thrips NA Thysanoptera: Thripidae Nebiker et al. 2016 Cinestar-8 MK Heavy Lift, Freefly Systems Eight rotors M Mini-MCA6, Tetracam Inc. 6 Arthropod counts, soil and plant tissue nutrient analysesc Canola Brassica spp. Green peach aphid Myzus persicae Hemiptera: Aphididae Severtson et al. 2016a Matrice 100, SZ DJI Technology Co. Four rotors M ADC-Lite, Tetracam Inc. 3 Damage assessments Cotton Gossypium hirsutum L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Huang et al. 2018 Spreading Wings S800, SZ DJI Technology Co. Six rotors M Mini-MCA6, Tetracam Inc. 6 Damage assessments Potato Solanum tuberosum Colorado potato beetle Leptinotarsa decemlineata Coleoptera: Chrysomelidae Hunt and Rondon 2017, Hunt et al. 2017 eBee, senseFly Fixed wing M S110 NIRb, Canon 3 Arthropod counts Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Stanton et al. 2017 aRGB = red green blue, M = multispectral, H = hyperspectral. bNIR = near infrared. cRemote sensing was used to detect nutrient deficiencies, which were correlated to arthropod presence. NA = information not provided. Open in new tab Table 2. Studies on aerial (manned aircraft) hyperspectral and multispectral remote sensing to detect arthropod-induced stress in crops and orchards Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M K-17, Fairchild Camera and Instrument Corp. + Hasselblad camera NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Ashby Hemiptera: Aleyrodidae Hart et al. 1973 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Hemiptera: Aleyrodidae Everitt et al. 1994 M K-17, Fairchild Camera and Instrument Corp. NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Brown soft scale Coccus hesperidum L. Hemiptera: Coccidae Hart and Meyers 1968 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M MS2100, DuncanTech 3 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 M CRSP, NASA 3 Sweep net sampling, drop cloth sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Palisot de Beauvois Hemiptera: Miridae Willers et al. 1999 M RDACS, ITDc, Stennis Space Center 3 Sweep net sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Hemiptera: Miridae Willers et al. 2005 M MS3100, DuncanTech 3 Damage assessments Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Elliott et al. 2015; Backoulou et al. 2018a, b M MS3100, DuncanTech 3 Visual inspections Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Backoulou et al. 2011a,b, 2013, 2016 M SSTCRIS, SST Development Group Inc. 3 Proportion of infested plants Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Elliott et al. 2007 M TerrAvion 2 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Say Diptera: Cecidomyiidae Bhattarai et al. 2019 M MS3100, DuncanTech 3 Arthropod counts or visual inspection Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Elliott et al. 2009; Backoulou et al. 2015, 2016 M CASI, Borstad Associates + EO Camera, NASA ARCd 4-8d Root digging Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Lobits et al. 1997 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H AVIRIS, NASA 224 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Ugarov and Nikolskii Acari: Tetranychidae Fitzgerald et al. 2004 H AISA, Specim Spectral Imaging Ltd. 50 Visual inspection of images Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2014 H RDACS-H4, ITDc, Stennis Space Center 120– 240 Damage assessments Corn Zea mays European corn borer Ostrinia nubilalis Lepidoptera: Crambidae Carroll et al. 2008 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M K-17, Fairchild Camera and Instrument Corp. + Hasselblad camera NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Ashby Hemiptera: Aleyrodidae Hart et al. 1973 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Hemiptera: Aleyrodidae Everitt et al. 1994 M K-17, Fairchild Camera and Instrument Corp. NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Brown soft scale Coccus hesperidum L. Hemiptera: Coccidae Hart and Meyers 1968 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M MS2100, DuncanTech 3 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 M CRSP, NASA 3 Sweep net sampling, drop cloth sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Palisot de Beauvois Hemiptera: Miridae Willers et al. 1999 M RDACS, ITDc, Stennis Space Center 3 Sweep net sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Hemiptera: Miridae Willers et al. 2005 M MS3100, DuncanTech 3 Damage assessments Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Elliott et al. 2015; Backoulou et al. 2018a, b M MS3100, DuncanTech 3 Visual inspections Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Backoulou et al. 2011a,b, 2013, 2016 M SSTCRIS, SST Development Group Inc. 3 Proportion of infested plants Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Elliott et al. 2007 M TerrAvion 2 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Say Diptera: Cecidomyiidae Bhattarai et al. 2019 M MS3100, DuncanTech 3 Arthropod counts or visual inspection Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Elliott et al. 2009; Backoulou et al. 2015, 2016 M CASI, Borstad Associates + EO Camera, NASA ARCd 4-8d Root digging Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Lobits et al. 1997 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H AVIRIS, NASA 224 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Ugarov and Nikolskii Acari: Tetranychidae Fitzgerald et al. 2004 H AISA, Specim Spectral Imaging Ltd. 50 Visual inspection of images Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2014 H RDACS-H4, ITDc, Stennis Space Center 120– 240 Damage assessments Corn Zea mays European corn borer Ostrinia nubilalis Lepidoptera: Crambidae Carroll et al. 2008 aM = multispectral, H = hyperspectral. bA fungus not infesting the plant, but growing on the arthropod’s sugary honeydew secretions. cInstitute of Technology and Development. dPrimary project sensors; five additional sensors were used with 3–8 spectral bands. NA = information not provided. Open in new tab Table 2. Studies on aerial (manned aircraft) hyperspectral and multispectral remote sensing to detect arthropod-induced stress in crops and orchards Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M K-17, Fairchild Camera and Instrument Corp. + Hasselblad camera NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Ashby Hemiptera: Aleyrodidae Hart et al. 1973 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Hemiptera: Aleyrodidae Everitt et al. 1994 M K-17, Fairchild Camera and Instrument Corp. NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Brown soft scale Coccus hesperidum L. Hemiptera: Coccidae Hart and Meyers 1968 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M MS2100, DuncanTech 3 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 M CRSP, NASA 3 Sweep net sampling, drop cloth sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Palisot de Beauvois Hemiptera: Miridae Willers et al. 1999 M RDACS, ITDc, Stennis Space Center 3 Sweep net sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Hemiptera: Miridae Willers et al. 2005 M MS3100, DuncanTech 3 Damage assessments Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Elliott et al. 2015; Backoulou et al. 2018a, b M MS3100, DuncanTech 3 Visual inspections Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Backoulou et al. 2011a,b, 2013, 2016 M SSTCRIS, SST Development Group Inc. 3 Proportion of infested plants Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Elliott et al. 2007 M TerrAvion 2 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Say Diptera: Cecidomyiidae Bhattarai et al. 2019 M MS3100, DuncanTech 3 Arthropod counts or visual inspection Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Elliott et al. 2009; Backoulou et al. 2015, 2016 M CASI, Borstad Associates + EO Camera, NASA ARCd 4-8d Root digging Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Lobits et al. 1997 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H AVIRIS, NASA 224 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Ugarov and Nikolskii Acari: Tetranychidae Fitzgerald et al. 2004 H AISA, Specim Spectral Imaging Ltd. 50 Visual inspection of images Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2014 H RDACS-H4, ITDc, Stennis Space Center 120– 240 Damage assessments Corn Zea mays European corn borer Ostrinia nubilalis Lepidoptera: Crambidae Carroll et al. 2008 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M K-17, Fairchild Camera and Instrument Corp. + Hasselblad camera NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Ashby Hemiptera: Aleyrodidae Hart et al. 1973 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Citrus Citrus spp. Citrus blackfly Aleurocanthus woglumi Hemiptera: Aleyrodidae Everitt et al. 1994 M K-17, Fairchild Camera and Instrument Corp. NA Arthropod counts, sooty mold assessmentsb Citrus Citrus spp. Brown soft scale Coccus hesperidum L. Hemiptera: Coccidae Hart and Meyers 1968 M System composed of 3 video cameras 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M MS2100, DuncanTech 3 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 M CRSP, NASA 3 Sweep net sampling, drop cloth sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Palisot de Beauvois Hemiptera: Miridae Willers et al. 1999 M RDACS, ITDc, Stennis Space Center 3 Sweep net sampling Cotton Gossypium hirsutum Tarnished plant bug Lygus lineolaris Hemiptera: Miridae Willers et al. 2005 M MS3100, DuncanTech 3 Damage assessments Sorghum Sorghum bicolor Sugarcane aphid Melanaphis sacchari Hemiptera: Aphididae Elliott et al. 2015; Backoulou et al. 2018a, b M MS3100, DuncanTech 3 Visual inspections Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Backoulou et al. 2011a,b, 2013, 2016 M SSTCRIS, SST Development Group Inc. 3 Proportion of infested plants Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Elliott et al. 2007 M TerrAvion 2 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Say Diptera: Cecidomyiidae Bhattarai et al. 2019 M MS3100, DuncanTech 3 Arthropod counts or visual inspection Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Elliott et al. 2009; Backoulou et al. 2015, 2016 M CASI, Borstad Associates + EO Camera, NASA ARCd 4-8d Root digging Grape Vitis vinifera Grape phylloxera Daktulosphaira vitifoliae Hemiptera: Phylloxeridae Lobits et al. 1997 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M + H SAMRSS + AVNIR, Opto- Knowledge Systems 4 + 60 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H AVIRIS, NASA 224 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Ugarov and Nikolskii Acari: Tetranychidae Fitzgerald et al. 2004 H AISA, Specim Spectral Imaging Ltd. 50 Visual inspection of images Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2014 H RDACS-H4, ITDc, Stennis Space Center 120– 240 Damage assessments Corn Zea mays European corn borer Ostrinia nubilalis Lepidoptera: Crambidae Carroll et al. 2008 aM = multispectral, H = hyperspectral. bA fungus not infesting the plant, but growing on the arthropod’s sugary honeydew secretions. cInstitute of Technology and Development. dPrimary project sensors; five additional sensors were used with 3–8 spectral bands. NA = information not provided. Open in new tab Table 3. Studies on orbital multispectral remote sensing to detect arthropod-induced stress in crops Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 M Terra, MODIS, NASA 36 Arthropod counts Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Norton Hymenoptera: Cephidae Lestina et al. 2016 M Sentinel-2, S2A-L1C, ESAb 13 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Diptera: Cecidomyiidae Bhattarai et al. 2019 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Arthropod counts, damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2014 M Landsat-8, NASA 9 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Ma et al. 2019 M Landsat-5 TM, NASA 7 Arthropod counts Wheat Triticum aestivum Aphid NA Hemiptera: Aphididae Huang et al. 2011 M RapidEye, Planet Labs 5 Arthropod counts Corn Zea mays Stem borer Busseola spp. Lepidoptera: Noctuidae Abdel-Rahman et al. 2017 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Damage assessments Corn Zea mays Oriental armywormd Mythimna separata Walkerd Lepidoptera: Noctuidae Zhang et al. 2016 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 M Terra, MODIS, NASA 36 Arthropod counts Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Norton Hymenoptera: Cephidae Lestina et al. 2016 M Sentinel-2, S2A-L1C, ESAb 13 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Diptera: Cecidomyiidae Bhattarai et al. 2019 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Arthropod counts, damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2014 M Landsat-8, NASA 9 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Ma et al. 2019 M Landsat-5 TM, NASA 7 Arthropod counts Wheat Triticum aestivum Aphid NA Hemiptera: Aphididae Huang et al. 2011 M RapidEye, Planet Labs 5 Arthropod counts Corn Zea mays Stem borer Busseola spp. Lepidoptera: Noctuidae Abdel-Rahman et al. 2017 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Damage assessments Corn Zea mays Oriental armywormd Mythimna separata Walkerd Lepidoptera: Noctuidae Zhang et al. 2016 aM = multispectral. bEuropean Space Agency. cNational Committee for Disaster Reduction and State Environmental Protection Administration of China. dThe arthropod species was originally misidentified as Spodoptera frugiperda; a correction was issued. NA = information not provided. Open in new tab Table 3. Studies on orbital multispectral remote sensing to detect arthropod-induced stress in crops Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 M Terra, MODIS, NASA 36 Arthropod counts Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Norton Hymenoptera: Cephidae Lestina et al. 2016 M Sentinel-2, S2A-L1C, ESAb 13 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Diptera: Cecidomyiidae Bhattarai et al. 2019 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Arthropod counts, damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2014 M Landsat-8, NASA 9 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Ma et al. 2019 M Landsat-5 TM, NASA 7 Arthropod counts Wheat Triticum aestivum Aphid NA Hemiptera: Aphididae Huang et al. 2011 M RapidEye, Planet Labs 5 Arthropod counts Corn Zea mays Stem borer Busseola spp. Lepidoptera: Noctuidae Abdel-Rahman et al. 2017 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Damage assessments Corn Zea mays Oriental armywormd Mythimna separata Walkerd Lepidoptera: Noctuidae Zhang et al. 2016 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006, 2010 M QuickBird, DigitalGlobe 3 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 M Terra, MODIS, NASA 36 Arthropod counts Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Norton Hymenoptera: Cephidae Lestina et al. 2016 M Sentinel-2, S2A-L1C, ESAb 13 Arthropod counts Wheat Triticum aestivum Hessian fly Mayetiola destructor Diptera: Cecidomyiidae Bhattarai et al. 2019 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Arthropod counts, damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2014 M Landsat-8, NASA 9 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Ma et al. 2019 M Landsat-5 TM, NASA 7 Arthropod counts Wheat Triticum aestivum Aphid NA Hemiptera: Aphididae Huang et al. 2011 M RapidEye, Planet Labs 5 Arthropod counts Corn Zea mays Stem borer Busseola spp. Lepidoptera: Noctuidae Abdel-Rahman et al. 2017 M HJ-1A/B, CCD sensor, NDRCC/SEPAc, 4 Damage assessments Corn Zea mays Oriental armywormd Mythimna separata Walkerd Lepidoptera: Noctuidae Zhang et al. 2016 aM = multispectral. bEuropean Space Agency. cNational Committee for Disaster Reduction and State Environmental Protection Administration of China. dThe arthropod species was originally misidentified as Spodoptera frugiperda; a correction was issued. NA = information not provided. Open in new tab Table 4. Studies on ground-based hyperspectral and multispectral remote sensing to detect arthropod-induced stress in crops and orchards Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M 12–1000 modular-multiband radiometer, Barnes Engineering Co. 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M System composed of visible and NIR ‘Varispec’ liquid-crystal tunable-filters, Cambridge Research Instrumentation Inc. + Pluto digital camera, PixelVision Inc. 68 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Acari: Tetranychidae Fitzgerald et al. 2004 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations or arthropod counts Cotton Gossypium spp. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin et al. 2015, Martin and Latheef 2017, 2018 M ADC, Tetracam Inc. 3 Controlled infestations Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Lan et al. 2013 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations Pinto bean Phaseolus vulgaris L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2018 M MSR 16 radiometer, Cropscan Inc. 16 Visual inspections or controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2012 M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Yang et al. 2009b M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Yang et al. 2005, 2009b M GreenSeeker optical sensor, Trimble Navigation 2 Damage assessments Corn Zea mays Banks grass mite + two-spotted spider mite Oligonychus pratensis Banks + Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2019 H MS-720 spectroradiometer, EKO Instruments Co., Ltd. 213 Visual inspections Pepper Capsicum annuum L. Chilli thrips Scirtothrips dorsalis Hood Thysanoptera: Thripidae Mohite et al. 2018 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Pepper Capsicum annuum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2012, 2015, 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Strawberry Fragaria × ananassa Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Fraulo et al. 2009 H FieldSpec 4 Hi-Res spectroradiometer, ASD 2,151 Arthropod counts Soybean Glycine max Soybean aphid Aphis glycines Hemiptera: Aphididae Alves et al. 2015, 2019 H FieldSpec 3, ASD 2,151 Damage assessments Soybean Glycine max Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Iost Filho 2019 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2007 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Chen et al. 2018 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Leafhopper NA Hemiptera: Cicadellidae Prabhakar et al. 2011 H FieldSpec spectroradiometer, ASD 2,151 Visual inspections Cotton Gossypium hirsutum Whitefly NA Hemiptera: Aleyrodidae Nigam et al. 2016 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments, sooty mold assessmentsb Cotton Gossypium hirsutum Solenopsis mealybug Phenacoccus solenopsis Tinsley Hemiptera: Pseudococcidae Prabhakar et al. 2013 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Lepidoptera: Noctuidae Sudbrink et al. 2003 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Cabbage looper Trichoplusia ni Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts or presence/ absence assessments Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Reisig and Godfrey 2007 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H SE590 spectroradiometer, Spectron Engineering, Inc. 252 Arthropod counts Apple Malus domestica European red mite Panonychus ulmi Koch Acari: Tetranychidae Peñuelas et al. 1995 H ImSpector V10E imaging spectograph, Specim Spectral Imaging Ltd. 512 Damage assessments Rice Oryza sativa Striped stem borer Chilo suppressalis Walker Lepidoptera: Crambidae Fan et al. 2017 H Fieldspec Full Range, ASD 2,151 Damage assessments, visual inspections or microscope analyses Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Guenee Lepidoptera: Crambidae Liu et al. 2012, 2018 H FieldSpec Handheld spectroradiometer, ASD 512 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Huang et al. 2012a H GER 2600 spectroradiometer, Spectral Vista Corp. 640 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Yang et al. 2007 H FieldSpec Handheld spectroradiometer, ASD 512 Arthropod counts or controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Stål Hemiptera: Delphacidae Huang et al. 2015a, Liu and Sun 2016, Tan et al. 2019 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Prasannakumar et al. 2013, 2014, Zhou et al. 2010 H GER 2600 spectroradiometer, Spectra Vista Corp. 640 Damage assessments Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Yang et al. 2007 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Bean Phaseolus vulgaris Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2017 H FieldSpec Pro spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Peach Prunus persica (L.) Batsch Spider mite Tetranychus spp. Acari: Tetranychidae Zhang et al. 2008, Luedeling et al. 2009 H FieldSpec 3 spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Sugarcane Saccharum spp. Sugarcane thrips Fulmekiola serrata Kobus Thysanoptera: Thripidae Abdel-Rahman et al. 2009, 2010, 2013 H Nexus FT-NIR spectrometer, Thermo Nicolet Corp. 531 Damage assessments Tomato Solanum lycopersicum L. Leafminer NA NA Xu et al. 2007 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Corn leaf aphid Rhopalosiphum maidis Fitch Hemiptera: Aphididae Li et al. 2008 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Greenbug Schizaphis graminum Hemiptera: Aphididae Li et al. 2008 H Hyperspectral camera, Resonon 213 Controlled infestations and arthropod presence confirmations Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Hymenoptera: Cephidae Nansen et al. 2009 H FieldSpec Handheld Spectroradiometer, ASD 512 Arthropod counts Wheat Triticum aestivum Sunn pest Eurygaster integriceps Puton Hemiptera: Scutelleridae Genc et al. 2008 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Riedell and Blackmer 1999 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts or controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Mirik et al. 2006a, b H Pushbroom imaging spectrometer (PIS), Beijing Research Center for Information Technology in Agriculture and University of Science and Technology of China 1,024 Arthropod counts or damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Zhao et al. 2012, Luo et al. 2013a H FieldSpec Pro spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2011; Huang et al. 2012b, 2013, 2014 H FieldSpec UV/VNIR spectroradiometer, ASD 2,151 Damage assessments or visual inspections Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Yuan et al. 2014, 2017; Zhang et al. 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2013b, c H FieldSpec spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Huang et al. 2014, Shi et al. 2017 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2007 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Riedell and Blackmer 1999 H Pika II hyperspectral imaging camera, Resonon 240 Controlled infestations Corn Zea mays Green belly stink bug Dichelops melacanthus Dallas Hemiptera: Pentatomidae Do Prado Ribeiro et al. 2018 H Pika II hyperspectral imaging camera, Resonon 160 Arthropod counts Corn Zea mays Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Nansen et al. 2010, Nansen 2012 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M 12–1000 modular-multiband radiometer, Barnes Engineering Co. 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M System composed of visible and NIR ‘Varispec’ liquid-crystal tunable-filters, Cambridge Research Instrumentation Inc. + Pluto digital camera, PixelVision Inc. 68 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Acari: Tetranychidae Fitzgerald et al. 2004 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations or arthropod counts Cotton Gossypium spp. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin et al. 2015, Martin and Latheef 2017, 2018 M ADC, Tetracam Inc. 3 Controlled infestations Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Lan et al. 2013 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations Pinto bean Phaseolus vulgaris L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2018 M MSR 16 radiometer, Cropscan Inc. 16 Visual inspections or controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2012 M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Yang et al. 2009b M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Yang et al. 2005, 2009b M GreenSeeker optical sensor, Trimble Navigation 2 Damage assessments Corn Zea mays Banks grass mite + two-spotted spider mite Oligonychus pratensis Banks + Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2019 H MS-720 spectroradiometer, EKO Instruments Co., Ltd. 213 Visual inspections Pepper Capsicum annuum L. Chilli thrips Scirtothrips dorsalis Hood Thysanoptera: Thripidae Mohite et al. 2018 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Pepper Capsicum annuum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2012, 2015, 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Strawberry Fragaria × ananassa Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Fraulo et al. 2009 H FieldSpec 4 Hi-Res spectroradiometer, ASD 2,151 Arthropod counts Soybean Glycine max Soybean aphid Aphis glycines Hemiptera: Aphididae Alves et al. 2015, 2019 H FieldSpec 3, ASD 2,151 Damage assessments Soybean Glycine max Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Iost Filho 2019 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2007 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Chen et al. 2018 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Leafhopper NA Hemiptera: Cicadellidae Prabhakar et al. 2011 H FieldSpec spectroradiometer, ASD 2,151 Visual inspections Cotton Gossypium hirsutum Whitefly NA Hemiptera: Aleyrodidae Nigam et al. 2016 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments, sooty mold assessmentsb Cotton Gossypium hirsutum Solenopsis mealybug Phenacoccus solenopsis Tinsley Hemiptera: Pseudococcidae Prabhakar et al. 2013 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Lepidoptera: Noctuidae Sudbrink et al. 2003 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Cabbage looper Trichoplusia ni Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts or presence/ absence assessments Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Reisig and Godfrey 2007 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H SE590 spectroradiometer, Spectron Engineering, Inc. 252 Arthropod counts Apple Malus domestica European red mite Panonychus ulmi Koch Acari: Tetranychidae Peñuelas et al. 1995 H ImSpector V10E imaging spectograph, Specim Spectral Imaging Ltd. 512 Damage assessments Rice Oryza sativa Striped stem borer Chilo suppressalis Walker Lepidoptera: Crambidae Fan et al. 2017 H Fieldspec Full Range, ASD 2,151 Damage assessments, visual inspections or microscope analyses Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Guenee Lepidoptera: Crambidae Liu et al. 2012, 2018 H FieldSpec Handheld spectroradiometer, ASD 512 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Huang et al. 2012a H GER 2600 spectroradiometer, Spectral Vista Corp. 640 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Yang et al. 2007 H FieldSpec Handheld spectroradiometer, ASD 512 Arthropod counts or controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Stål Hemiptera: Delphacidae Huang et al. 2015a, Liu and Sun 2016, Tan et al. 2019 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Prasannakumar et al. 2013, 2014, Zhou et al. 2010 H GER 2600 spectroradiometer, Spectra Vista Corp. 640 Damage assessments Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Yang et al. 2007 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Bean Phaseolus vulgaris Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2017 H FieldSpec Pro spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Peach Prunus persica (L.) Batsch Spider mite Tetranychus spp. Acari: Tetranychidae Zhang et al. 2008, Luedeling et al. 2009 H FieldSpec 3 spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Sugarcane Saccharum spp. Sugarcane thrips Fulmekiola serrata Kobus Thysanoptera: Thripidae Abdel-Rahman et al. 2009, 2010, 2013 H Nexus FT-NIR spectrometer, Thermo Nicolet Corp. 531 Damage assessments Tomato Solanum lycopersicum L. Leafminer NA NA Xu et al. 2007 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Corn leaf aphid Rhopalosiphum maidis Fitch Hemiptera: Aphididae Li et al. 2008 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Greenbug Schizaphis graminum Hemiptera: Aphididae Li et al. 2008 H Hyperspectral camera, Resonon 213 Controlled infestations and arthropod presence confirmations Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Hymenoptera: Cephidae Nansen et al. 2009 H FieldSpec Handheld Spectroradiometer, ASD 512 Arthropod counts Wheat Triticum aestivum Sunn pest Eurygaster integriceps Puton Hemiptera: Scutelleridae Genc et al. 2008 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Riedell and Blackmer 1999 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts or controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Mirik et al. 2006a, b H Pushbroom imaging spectrometer (PIS), Beijing Research Center for Information Technology in Agriculture and University of Science and Technology of China 1,024 Arthropod counts or damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Zhao et al. 2012, Luo et al. 2013a H FieldSpec Pro spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2011; Huang et al. 2012b, 2013, 2014 H FieldSpec UV/VNIR spectroradiometer, ASD 2,151 Damage assessments or visual inspections Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Yuan et al. 2014, 2017; Zhang et al. 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2013b, c H FieldSpec spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Huang et al. 2014, Shi et al. 2017 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2007 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Riedell and Blackmer 1999 H Pika II hyperspectral imaging camera, Resonon 240 Controlled infestations Corn Zea mays Green belly stink bug Dichelops melacanthus Dallas Hemiptera: Pentatomidae Do Prado Ribeiro et al. 2018 H Pika II hyperspectral imaging camera, Resonon 160 Arthropod counts Corn Zea mays Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Nansen et al. 2010, Nansen 2012 aM = multispectral, H = hyperspectral. bA fungus not infesting the plant, but growing on the arthropod’s sugary honeydew secretions. NA = information not provided. Open in new tab Table 4. Studies on ground-based hyperspectral and multispectral remote sensing to detect arthropod-induced stress in crops and orchards Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M 12–1000 modular-multiband radiometer, Barnes Engineering Co. 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M System composed of visible and NIR ‘Varispec’ liquid-crystal tunable-filters, Cambridge Research Instrumentation Inc. + Pluto digital camera, PixelVision Inc. 68 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Acari: Tetranychidae Fitzgerald et al. 2004 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations or arthropod counts Cotton Gossypium spp. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin et al. 2015, Martin and Latheef 2017, 2018 M ADC, Tetracam Inc. 3 Controlled infestations Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Lan et al. 2013 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations Pinto bean Phaseolus vulgaris L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2018 M MSR 16 radiometer, Cropscan Inc. 16 Visual inspections or controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2012 M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Yang et al. 2009b M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Yang et al. 2005, 2009b M GreenSeeker optical sensor, Trimble Navigation 2 Damage assessments Corn Zea mays Banks grass mite + two-spotted spider mite Oligonychus pratensis Banks + Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2019 H MS-720 spectroradiometer, EKO Instruments Co., Ltd. 213 Visual inspections Pepper Capsicum annuum L. Chilli thrips Scirtothrips dorsalis Hood Thysanoptera: Thripidae Mohite et al. 2018 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Pepper Capsicum annuum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2012, 2015, 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Strawberry Fragaria × ananassa Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Fraulo et al. 2009 H FieldSpec 4 Hi-Res spectroradiometer, ASD 2,151 Arthropod counts Soybean Glycine max Soybean aphid Aphis glycines Hemiptera: Aphididae Alves et al. 2015, 2019 H FieldSpec 3, ASD 2,151 Damage assessments Soybean Glycine max Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Iost Filho 2019 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2007 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Chen et al. 2018 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Leafhopper NA Hemiptera: Cicadellidae Prabhakar et al. 2011 H FieldSpec spectroradiometer, ASD 2,151 Visual inspections Cotton Gossypium hirsutum Whitefly NA Hemiptera: Aleyrodidae Nigam et al. 2016 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments, sooty mold assessmentsb Cotton Gossypium hirsutum Solenopsis mealybug Phenacoccus solenopsis Tinsley Hemiptera: Pseudococcidae Prabhakar et al. 2013 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Lepidoptera: Noctuidae Sudbrink et al. 2003 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Cabbage looper Trichoplusia ni Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts or presence/ absence assessments Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Reisig and Godfrey 2007 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H SE590 spectroradiometer, Spectron Engineering, Inc. 252 Arthropod counts Apple Malus domestica European red mite Panonychus ulmi Koch Acari: Tetranychidae Peñuelas et al. 1995 H ImSpector V10E imaging spectograph, Specim Spectral Imaging Ltd. 512 Damage assessments Rice Oryza sativa Striped stem borer Chilo suppressalis Walker Lepidoptera: Crambidae Fan et al. 2017 H Fieldspec Full Range, ASD 2,151 Damage assessments, visual inspections or microscope analyses Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Guenee Lepidoptera: Crambidae Liu et al. 2012, 2018 H FieldSpec Handheld spectroradiometer, ASD 512 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Huang et al. 2012a H GER 2600 spectroradiometer, Spectral Vista Corp. 640 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Yang et al. 2007 H FieldSpec Handheld spectroradiometer, ASD 512 Arthropod counts or controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Stål Hemiptera: Delphacidae Huang et al. 2015a, Liu and Sun 2016, Tan et al. 2019 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Prasannakumar et al. 2013, 2014, Zhou et al. 2010 H GER 2600 spectroradiometer, Spectra Vista Corp. 640 Damage assessments Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Yang et al. 2007 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Bean Phaseolus vulgaris Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2017 H FieldSpec Pro spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Peach Prunus persica (L.) Batsch Spider mite Tetranychus spp. Acari: Tetranychidae Zhang et al. 2008, Luedeling et al. 2009 H FieldSpec 3 spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Sugarcane Saccharum spp. Sugarcane thrips Fulmekiola serrata Kobus Thysanoptera: Thripidae Abdel-Rahman et al. 2009, 2010, 2013 H Nexus FT-NIR spectrometer, Thermo Nicolet Corp. 531 Damage assessments Tomato Solanum lycopersicum L. Leafminer NA NA Xu et al. 2007 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Corn leaf aphid Rhopalosiphum maidis Fitch Hemiptera: Aphididae Li et al. 2008 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Greenbug Schizaphis graminum Hemiptera: Aphididae Li et al. 2008 H Hyperspectral camera, Resonon 213 Controlled infestations and arthropod presence confirmations Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Hymenoptera: Cephidae Nansen et al. 2009 H FieldSpec Handheld Spectroradiometer, ASD 512 Arthropod counts Wheat Triticum aestivum Sunn pest Eurygaster integriceps Puton Hemiptera: Scutelleridae Genc et al. 2008 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Riedell and Blackmer 1999 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts or controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Mirik et al. 2006a, b H Pushbroom imaging spectrometer (PIS), Beijing Research Center for Information Technology in Agriculture and University of Science and Technology of China 1,024 Arthropod counts or damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Zhao et al. 2012, Luo et al. 2013a H FieldSpec Pro spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2011; Huang et al. 2012b, 2013, 2014 H FieldSpec UV/VNIR spectroradiometer, ASD 2,151 Damage assessments or visual inspections Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Yuan et al. 2014, 2017; Zhang et al. 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2013b, c H FieldSpec spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Huang et al. 2014, Shi et al. 2017 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2007 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Riedell and Blackmer 1999 H Pika II hyperspectral imaging camera, Resonon 240 Controlled infestations Corn Zea mays Green belly stink bug Dichelops melacanthus Dallas Hemiptera: Pentatomidae Do Prado Ribeiro et al. 2018 H Pika II hyperspectral imaging camera, Resonon 160 Arthropod counts Corn Zea mays Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Nansen et al. 2010, Nansen 2012 Spectral resolutiona . Sensor details . No. of spectral bands . Field observations . Plant common name . Plant species . Arthropod common name . Arthropod species . Order: Family . References . M 12–1000 modular-multiband radiometer, Barnes Engineering Co. 3 Visual inspections, sooty mold assessmentsb Cotton Gossypium hirsutum Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Everitt et al. 1996 M System composed of visible and NIR ‘Varispec’ liquid-crystal tunable-filters, Cambridge Research Instrumentation Inc. + Pluto digital camera, PixelVision Inc. 68 Arthropod counts Cotton Gossypium hirsutum Strawberry spider mite Tetranychus turkestani Acari: Tetranychidae Fitzgerald et al. 2004 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations or arthropod counts Cotton Gossypium spp. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin et al. 2015, Martin and Latheef 2017, 2018 M ADC, Tetracam Inc. 3 Controlled infestations Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Lan et al. 2013 M Model 505 GreenSeeker optical sensor, Trimble Navigation 2 Controlled infestations Pinto bean Phaseolus vulgaris L. Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2018 M MSR 16 radiometer, Cropscan Inc. 16 Visual inspections or controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2012 M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Yang et al. 2009b M MSR 16R radiometer, Cropscan Inc. 16 Arthropod counts Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Yang et al. 2005, 2009b M GreenSeeker optical sensor, Trimble Navigation 2 Damage assessments Corn Zea mays Banks grass mite + two-spotted spider mite Oligonychus pratensis Banks + Tetranychus urticae Acari: Tetranychidae Martin and Latheef 2019 H MS-720 spectroradiometer, EKO Instruments Co., Ltd. 213 Visual inspections Pepper Capsicum annuum L. Chilli thrips Scirtothrips dorsalis Hood Thysanoptera: Thripidae Mohite et al. 2018 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Pepper Capsicum annuum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2012, 2015, 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Strawberry Fragaria × ananassa Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Fraulo et al. 2009 H FieldSpec 4 Hi-Res spectroradiometer, ASD 2,151 Arthropod counts Soybean Glycine max Soybean aphid Aphis glycines Hemiptera: Aphididae Alves et al. 2015, 2019 H FieldSpec 3, ASD 2,151 Damage assessments Soybean Glycine max Silverleaf whitefly Bemisia tabaci Hemiptera: Aleyrodidae Iost Filho 2019 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2006 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Reisig and Godfrey 2007 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Cotton aphid Aphis gossypii Hemiptera: Aphididae Chen et al. 2018 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments Cotton Gossypium hirsutum Leafhopper NA Hemiptera: Cicadellidae Prabhakar et al. 2011 H FieldSpec spectroradiometer, ASD 2,151 Visual inspections Cotton Gossypium hirsutum Whitefly NA Hemiptera: Aleyrodidae Nigam et al. 2016 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Damage assessments, sooty mold assessmentsb Cotton Gossypium hirsutum Solenopsis mealybug Phenacoccus solenopsis Tinsley Hemiptera: Pseudococcidae Prabhakar et al. 2013 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Beet armyworm Spodoptera exigua Lepidoptera: Noctuidae Sudbrink et al. 2003 H GER 1500 spectroradiometer, Spectra Vista Corp. 512 Arthropod counts Cotton Gossypium hirsutum Cabbage looper Trichoplusia ni Hübner Lepidoptera: Noctuidae Sudbrink et al. 2003 H FieldSpec Pro FR spectrometer, ASD + GER 1500 spectroradiometer, Spectra Vista Corp. 2,151 + 512 Arthropod counts or presence/ absence assessments Cotton Gossypium hirsutum Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Reisig and Godfrey 2007 H FieldSpec Pro FR spectrometer, ASD 2,151 Arthropod counts Cotton Gossypium hirsutum Spider mite Tetranychus spp. Acari: Tetranychidae Reisig and Godfrey 2006 H SE590 spectroradiometer, Spectron Engineering, Inc. 252 Arthropod counts Apple Malus domestica European red mite Panonychus ulmi Koch Acari: Tetranychidae Peñuelas et al. 1995 H ImSpector V10E imaging spectograph, Specim Spectral Imaging Ltd. 512 Damage assessments Rice Oryza sativa Striped stem borer Chilo suppressalis Walker Lepidoptera: Crambidae Fan et al. 2017 H Fieldspec Full Range, ASD 2,151 Damage assessments, visual inspections or microscope analyses Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Guenee Lepidoptera: Crambidae Liu et al. 2012, 2018 H FieldSpec Handheld spectroradiometer, ASD 512 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Huang et al. 2012a H GER 2600 spectroradiometer, Spectral Vista Corp. 640 Damage assessments Rice Oryza sativa Rice leaf folder Cnaphalocrocis medinalis Lepidoptera: Crambidae Yang et al. 2007 H FieldSpec Handheld spectroradiometer, ASD 512 Arthropod counts or controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Stål Hemiptera: Delphacidae Huang et al. 2015a, Liu and Sun 2016, Tan et al. 2019 H FieldSpec 3 Hi-Res spectroradiometer, ASD 2,151 Controlled infestations Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Prasannakumar et al. 2013, 2014, Zhou et al. 2010 H GER 2600 spectroradiometer, Spectra Vista Corp. 640 Damage assessments Rice Oryza sativa Brown planthopper Nilaparvata lugens Hemiptera: Delphacidae Yang et al. 2007 H FieldSpec Pro FR spectroradiometer, ASD 2,151 Damage assessments Bean Phaseolus vulgaris Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Herrmann et al. 2017 H FieldSpec Pro spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Peach Prunus persica (L.) Batsch Spider mite Tetranychus spp. Acari: Tetranychidae Zhang et al. 2008, Luedeling et al. 2009 H FieldSpec 3 spectroradiometer, ASD 2,151 Arthropod counts or damage assessments Sugarcane Saccharum spp. Sugarcane thrips Fulmekiola serrata Kobus Thysanoptera: Thripidae Abdel-Rahman et al. 2009, 2010, 2013 H Nexus FT-NIR spectrometer, Thermo Nicolet Corp. 531 Damage assessments Tomato Solanum lycopersicum L. Leafminer NA NA Xu et al. 2007 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Corn leaf aphid Rhopalosiphum maidis Fitch Hemiptera: Aphididae Li et al. 2008 H HR2000 spectroradiometer, Ocean Optics Inc. 62 Arthropod counts Sorghum Sorghum bicolor Greenbug Schizaphis graminum Hemiptera: Aphididae Li et al. 2008 H Hyperspectral camera, Resonon 213 Controlled infestations and arthropod presence confirmations Wheat Triticum aestivum Wheat stem sawfly Cephus cinctus Hymenoptera: Cephidae Nansen et al. 2009 H FieldSpec Handheld Spectroradiometer, ASD 512 Arthropod counts Wheat Triticum aestivum Sunn pest Eurygaster integriceps Puton Hemiptera: Scutelleridae Genc et al. 2008 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Riedell and Blackmer 1999 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts or controlled infestations Wheat Triticum aestivum Greenbug Schizaphis graminum Hemiptera: Aphididae Mirik et al. 2006a, b H Pushbroom imaging spectrometer (PIS), Beijing Research Center for Information Technology in Agriculture and University of Science and Technology of China 1,024 Arthropod counts or damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Zhao et al. 2012, Luo et al. 2013a H FieldSpec Pro spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2011; Huang et al. 2012b, 2013, 2014 H FieldSpec UV/VNIR spectroradiometer, ASD 2,151 Damage assessments or visual inspections Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Yuan et al. 2014, 2017; Zhang et al. 2017 H FieldSpec FR spectroradiometer, ASD 2,151 Arthropod counts Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Luo et al. 2013b, c H FieldSpec spectroradiometer, ASD 2,151 Damage assessments Wheat Triticum aestivum Wheat aphid Sitobion avenae Hemiptera: Aphididae Huang et al. 2014, Shi et al. 2017 H S2000 spectrometer, Ocean Optics Inc. 2,048 Arthropod counts Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Mirik et al. 2007 H Personal Spectrometer II, ASD 512 Controlled infestations Wheat Triticum aestivum Russian wheat aphid Diuraphis noxia Hemiptera: Aphididae Riedell and Blackmer 1999 H Pika II hyperspectral imaging camera, Resonon 240 Controlled infestations Corn Zea mays Green belly stink bug Dichelops melacanthus Dallas Hemiptera: Pentatomidae Do Prado Ribeiro et al. 2018 H Pika II hyperspectral imaging camera, Resonon 160 Arthropod counts Corn Zea mays Two-spotted spider mite Tetranychus urticae Acari: Tetranychidae Nansen et al. 2010, Nansen 2012 aM = multispectral, H = hyperspectral. bA fungus not infesting the plant, but growing on the arthropod’s sugary honeydew secretions. NA = information not provided. Open in new tab In these tables, optical sensors are grouped, in addition to the platform, they are mounted on, into RGB, multispectral, and hyperspectral sensors. As stated above, generally, multispectral sensors have 3–12 broad spectral bands at selected wavelength ranges, whereas hyperspectral sensors have many (usually >20, but up to several hundreds) narrow, contiguous spectral bands, acquiring the spectrum within the selected spectral region with many measurement points. However, there is no clear agreed on definition. Therefore, the tables include multispectral sensors acquiring more than 12 spectral bands. While grouping the sensors, we adhered to the authors’ classifications (Tables 1–4). Tables 1–4 focus on detection of arthropod pests; we did not address diseases caused by arthropod vectors (e.g., Garcia-Ruiz et al. 2013). Also, these tables only contain studies related to crops and orchards. We did not address forestry studies, as the body of literature on pest detection involves multi-species forests, adding an additional layer of complexity as opposed to crops and orchards in monoculture. More information about remote sensing in forestry settings can be found elsewhere (Dash et al. 2016, Pádua et al. 2017, Stone and Mohammed 2017, Dash et al. 2018). It is important to note that with remote sensing, not the pests themselves are detected, but patterns of canopy reflectance that are indicative of arthropod-induced plant stress. Field observations to confirm the presence of specific stressors remain necessary, but field scouting can be more efficiently focused with the a priori knowledge from remote sensing. Analysis of Reflectance Spectra For the detection of plant stress using remote sensing, the spectral reflectance (the spectral signature or spectrum) of the vegetation is analyzed. Figure 3 shows a spectrum of healthy soybean leaves as recorded by a ground-based hyperspectral field spectrometer, together with the same spectrum resampled to the spectral resolution of a hyperspectral imaging spectrometer for drones, and a multispectral sensor for drones. The figure shows the large loss of information between a hyperspectral sensor and a multispectral sensor. With higher spectral resolutions (i.e., more spectral bands), detailed spectral characteristics become visible and can be used to analyze vegetation spectra. This analysis can be done in various ways, e.g., by analyzing spectral reflectance features (e.g., absorption bands or reflectance peaks) that can be directly related to plant physiology, or indirectly by building vegetation indices (VIs). These two techniques are addressed below exemplarily. An overview of techniques to quantify vegetation biophysical variables using imaging spectroscopy is given in Verrelst et al. (2019). Fig. 3. Open in new tabDownload slide Spectra of soybean leaves at different spectral resolutions. (a) As recorded by a handheld spectrometer with 1 nm spectral resolution (e.g., FieldSpec, ASD Inc., Boulder, CO). (b) Resampled to the spectral resolution of a hyperspectral imaging spectrometer (3–4 nm spectral resolution, e.g., OCI Imager, BaySpec, San Jose, CA). (c) Resampled to the spectral resolution of a multispectral sensor (four spectral bands, e.g., Parrot Sequoia, Parrot, Paris, France). Fig. 3. Open in new tabDownload slide Spectra of soybean leaves at different spectral resolutions. (a) As recorded by a handheld spectrometer with 1 nm spectral resolution (e.g., FieldSpec, ASD Inc., Boulder, CO). (b) Resampled to the spectral resolution of a hyperspectral imaging spectrometer (3–4 nm spectral resolution, e.g., OCI Imager, BaySpec, San Jose, CA). (c) Resampled to the spectral resolution of a multispectral sensor (four spectral bands, e.g., Parrot Sequoia, Parrot, Paris, France). Spectral Features and VIs An important spectral feature light region is the red edge, i.e., the slope between the red and near infrared region of the spectrum, around 700 nm. This spectral region relates to the chlorophyll concentration (Horler et al. 1983, Delegido et al. 2011, Huang et al. 2015b) and the Leaf Area Index (LAI), the area of green leaves per unit of ground area (Delegido et al. 2013). The red edge position (REP), the point of maximum slope in the red edge region, is a valuable indicator of stress and senescence (Das et al. 2014, Verrelst et al. 2019), possibly because various stressors decrease leaf chlorophyll concentrations (Carter and Knapp 2001). For instance, an increased reflectance around 740 nm is associated with spider mite susceptibility in corn (Zea mays L.) (Nansen et al. 2013). Also, the overall reflection level of the spectrum might be characteristic. It should be noted that a spectrum of an imaging spectrometer, such as one mounted on drones, always describes an area, not a point. This area, or pixel size, depends on the flight height of the drone and can range from less than 1 cm2 to more than 10 cm2. With larger pixels, the recorded spectrum consists of reflectance of both the plant and the soil (mixed pixels). This should be considered when analyzing the spectrum. Wherever possible, pixels that represent soil or other types of non-canopy area are excluded from data analysis. Various VIs assist in interpreting remote sensing data (Roberts et al. 2001, Xue and Su 2017, Verrelst et al. 2019). These are mainly ratios between multiple spectral bands (Glenn et al. 2008). An often-used index is the Normalized Difference Vegetation Index (NDVI), which incorporates the ratio of NIR and visible red light. Compared to a healthy plant, an unhealthy plant will generally reflect more visible light and less NIR light. In farming, the NDVI can be used as a predictor of plant physiological status, as well as potential yield (Peñuelas and Filella 1998). NDVI has its limitations, e.g., when there is a lot of soil in the background. To solve that issue, other VIs have been developed, such as the Soil Adjusted Vegetation Index (SAVI) (Huete et al. 1988). Where these two indices are broadband indices (i.e., they can be calculated with multispectral data), hyperspectral data allows for narrowband VIs that can more precisely focus on a specific aspect. An example is the Modified Chlorophyll Absorption in Reflectance Index (MCARI), which is defined to be maximally sensitive to chlorophyll content (Daughtry et al. 2000). Xue and Su (2017) provide a review of over 100 VIs for vegetation analysis. Classification Accuracy Classification algorithms, which could be based on the red edge and/or VIs, can be developed to group plants based on spectral data by relating field observations to spectral measurements (e.g., ‘healthy’ and ‘pest-infested’ plants). The algorithms can be based on various statistical approaches (Lowe et al. 2017). Classification accuracy is high if data has high robustness or repeatability. Different remote sensing studies report different classification accuracies (Lowe et al. 2017). A recent study with drone-based remote sensing to detect susceptibility against green peach aphid [Myzus persicae Sulzer (Hemiptera: Aphididae)] in canola, using a multispectral sensor mounted on an octocopter, a drone with eight rotors, reported a classification accuracy of 69–100%. These values depended on experimental day, drone height above the canopy, and whether or not non-leaf pixels were removed from the dataset. In this study, aphid infestations happened naturally, and aphids were counted on selected plants for ground verification of infestations (Severtson et al. 2016a). A study involving two-spotted spider mite-induced stress in cotton (Gossypium spp.), using a multispectral sensor mounted on a quadcopter, a drone with four rotors, reported a classification accuracy of 74–95%. These values depended on classification methods. Spider mite infestation levels were estimated based on plant damage (Huang et al. 2018). As it is hard to reach 100% accuracy, especially when data are obtained on different days, in most studies, there are certain numbers of false positives (plants are classified as infested while they are healthy) and/or false negatives (plants are classified as healthy while they are infested) (Congalton 1991, Lowe et al. 2017). Nevertheless, multiple robust classifications have been developed to detect pest problems in different agro-ecosystems, which provide good indicators for field scouting (Tables 1–4). Drones, Remote Sensing, and Arthropod Pests Everitt et al. (2003) provided an overview of the potential use of remote sensing data collected in a manned aircraft for pest management. The authors mapped four different pest-host systems (citrus orchards, cotton crops, forests, and rangelands), and concluded that aerial photography and videography could be used to detect arthropod infestations in both agricultural and natural environments (Everitt et al. 1994, 1996). With the development of unmanned aircrafts, it has become more affordable and practically feasible to collect aerial remote sensing data. A recent study with drone-based remote sensing to detect crop pests includes stress induced by sugarcane aphid [Melanaphis sacchari Zehntner (Hemiptera: Aphididae)] in sorghum (Sorghum bicolor (L.) Moench), using a multispectral sensor mounted on a fixed-wing drone. Aphids were counted throughout the growing season for ground verification of infestations, and damage was assessed as coverage with sooty mold, a fungus not infesting the plant, but growing on the aphids’ sugary honeydew secretions (Stanton et al. 2017). Colorado potato beetle [Leptinotarsa decemlineata Say (Coleoptera: Chrysomelidae)] damage in potato (Solanum tuberosum L.) has been assessed using a multispectral sensor mounted on a hexacopter, a drone with six rotors. Plants were infested with different numbers of beetles, and insects were counted and plant damage was visually assessed for ground verification of pest infestations (Hunt et al. 2016, Hunt and Rondon 2017) (Table 1). A study by F. Iost Filho, MSc, Dr. P. Yamamoto, and collaborators at the University of São Paulo, Brazil, is analyzing the effects of stress induced by several arthropod pests in soybean fields, including silverleaf whitefly [Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae)], stink bugs (Hemiptera: Pentatomidae), and caterpillars (Lepidoptera: Noctuidae). The system is composed of a drone-based multispectral sensor and a ground-based hyperspectral sensor (Iost Filho 2019). Researchers at the University of Wisconsin, WI are currently using a quadcopter equipped with a multispectral sensor to detect caterpillar damage in cranberry (Vaccinium macrocarpon Aiton) (Seely 2018). An ongoing study by Dr. E. de Lange, Dr. C. Nansen and collaborators at the University of California Davis, CA involves detection of stress induced by two-spotted spider mite in strawberry (Fragaria×ananassa Duchesne), using an octocopter equipped with a hyperspectral sensor (Fig. 4). Furthermore, aerial remote sensing can help distinguish between different non-crop plant species. If these plant species were differentially preferred as alternate hosts by important pests, remote sensing could contribute to vegetation management decisions (Sudbrink et al. 2015). Fig. 4. Open in new tabDownload slide Airborne remote sensing in California strawberry. Researchers from the University of California Davis obtain canopy reflectance data of arthropod-infested plants with a drone-mounted hyperspectral sensor in a commercial strawberry field. Fig. 4. Open in new tabDownload slide Airborne remote sensing in California strawberry. Researchers from the University of California Davis obtain canopy reflectance data of arthropod-infested plants with a drone-mounted hyperspectral sensor in a commercial strawberry field. Barbedo (2019) compiled a list of drone-based remote sensing studies for various applications, including detection of pests, pathogens, drought, and nutrient deficiencies. Drones are increasingly used for remote sensing studies and are particularly cost-efficient for inspections of smaller fields (Matese et al. 2015). As technology improves and costs decrease, they may also become more competitive for use in larger fields. Ultimately, usefulness of drone-based remote sensing for detection of pest problems will depend on individual grower needs. Distinguishing Multiple Stressors With Remote Sensing Most of the above-mentioned studies are based on a system composed of one arthropod pest species and one specific crop. However, when multiple arthropod pests are present, more advanced methods of data calibration and analysis are necessary. Prabhakar et al. (2012) inferred that damage by different pests on the same host plant requires a combination of multiple spectral bands for accurate detection. Indeed, a greenhouse study in wheat (Triticum aestivum L.) showed that reflectance data could be used to differentiate between two different pests. Plants were experimentally infested with greenbugs [Schizaphis graminum Rondani (Hemiptera: Aphididae)] or Russian wheat aphids [Diuraphis noxia Kurdjumov (Hemiptera: Aphididae)], and insects were counted on a regular basis. The authors did mention that additional field studies would be needed, as other stressors could result in similar symptoms as aphid infestations (Yang et al. 2009b). A field study in wheat used reflectance data to differentiate between arthropod [wheat aphid, Sitobion avenae Fabricius (Hemiptera: Aphididae)] and pathogen (yellow rust, Puccinia striiformis Westend. f. sp. tritici Eriks and powdery mildew, Blumeria graminis (DC.) Speer) infestations. Aphids occurred naturally in the field, and pathogens were inoculated; for all three stressors, damage levels were estimated. Overall classification accuracy was 76% (Yuan et al. 2014). Another field study in wheat used reflectance data to distinguish between arthropod infestations (Russian wheat aphid) and abiotic stressors (drought and agronomic conditions, possibly poor tillage, germination, or fertilization). The different stressors were verified onsite (Backoulou et al. 2011b). However, laboratory and field studies on cotton plants exposed difficulties distinguishing two arthropod pests, cotton aphid [Aphis gossypii Glover (Hemiptera: Aphididae)] and two-spotted spider mite, based on spectral signatures. In these studies, plants were experimentally infested, and insects were counted, or their presence or absence was assessed, over time (Reisig and Godfrey 2007). It also proved difficult to separate nitrogen deficiencies and aphid infestations in cotton field studies. In these studies, aphids were naturally present, and plots were treated with pesticides to increase aphid populations, presumably by killing natural enemies. Aphids were counted throughout the experimental period. Different amounts of nitrogen were applied, which was verified with soil samples and analysis of plant nitrogen uptake (Reisig and Godfrey 2010). An overview of the few studies on hyperspectral and multispectral sensors to distinguish various biotic and abiotic stressors can be found in Table 5. Spectral indices that accurately predict the presence of various arthropod pests, as well as distinguish arthropod-induced stress from other sources of stress, are required for a large number of crops in order to be widely used in precision agriculture (Mulla 2013). Table 5. Studies on hyperspectral and multispectral remote sensing to distinguish various biotic and abiotic stressors in crops Platform . Spectral resolutiona . Sensor details . No. of spectral bands . Plant common name . Stress 1 . Stress 2 . Stress 3 . Field observations 1 . Field observations 2 . Field observations 3 . References . Ground-based M MSR 16R, Cropscan Inc. 16 Wheat Russian wheat aphid Greenbug - Arthropod counts Arthropod counts - Yang et al. 2009b Ground-based H Pika II, Resonon 160 Corn Two-spotted spider mite Drought stress - Arthropod counts Different irrigation levels - Nansen et al. 2010, Nansen 2012 Ground-based H FieldSpec Pro FR, ASD 2,151 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Ground-based H FieldSpec Pro FR, ASD + GER 1500, Spectra Vista Corp. 2,151 + 512 Cotton Cotton aphid Two-spotted spider mite - Arthropod counts Arthropod counts or presence/ absence assessments - Reisig and Godfrey 2007 Ground-based H FieldSpec Handheld, ASD 512 Rice Brown planthopper Nitrogen stress - Arthropod counts Different fertilizer levels - Huang et al. 2015a Ground-based H Personal Spectrometer II, ASD 512 Wheat Russian wheat aphid Greenbug - Controlled infestations Controlled infestations - Riedell and Blackmer 1999 Ground-based H FieldSpec, FieldSpec Pro, or FieldSpec UV/VNIR, ASD 2,151 Wheat Wheat aphid Yellow rust Powdery mildew Damage assessments Damage assessments Damage assessments Huang et al. 2014; Yuan et al. 2014, 2017, Shi et al. 2017, Zhang et al. 2017 Aerial – manned aircraft M MS3100, Duncan Tech 3 Wheat Russian wheat aphid Greenbug - Visual inspections Visual inspections - Backoulou et al. 2016 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Other factorsb - Visual inspections Visual inspections - Backoulou et al. 2013 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2011a, b Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Greenbug Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2015 Aerial – Manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Aerial – manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M Landsat-8, NASA 9 Wheat Wheat aphid Powdery mildew - Arthropod counts Damage assessments - Ma et al. 2019 Platform . Spectral resolutiona . Sensor details . No. of spectral bands . Plant common name . Stress 1 . Stress 2 . Stress 3 . Field observations 1 . Field observations 2 . Field observations 3 . References . Ground-based M MSR 16R, Cropscan Inc. 16 Wheat Russian wheat aphid Greenbug - Arthropod counts Arthropod counts - Yang et al. 2009b Ground-based H Pika II, Resonon 160 Corn Two-spotted spider mite Drought stress - Arthropod counts Different irrigation levels - Nansen et al. 2010, Nansen 2012 Ground-based H FieldSpec Pro FR, ASD 2,151 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Ground-based H FieldSpec Pro FR, ASD + GER 1500, Spectra Vista Corp. 2,151 + 512 Cotton Cotton aphid Two-spotted spider mite - Arthropod counts Arthropod counts or presence/ absence assessments - Reisig and Godfrey 2007 Ground-based H FieldSpec Handheld, ASD 512 Rice Brown planthopper Nitrogen stress - Arthropod counts Different fertilizer levels - Huang et al. 2015a Ground-based H Personal Spectrometer II, ASD 512 Wheat Russian wheat aphid Greenbug - Controlled infestations Controlled infestations - Riedell and Blackmer 1999 Ground-based H FieldSpec, FieldSpec Pro, or FieldSpec UV/VNIR, ASD 2,151 Wheat Wheat aphid Yellow rust Powdery mildew Damage assessments Damage assessments Damage assessments Huang et al. 2014; Yuan et al. 2014, 2017, Shi et al. 2017, Zhang et al. 2017 Aerial – manned aircraft M MS3100, Duncan Tech 3 Wheat Russian wheat aphid Greenbug - Visual inspections Visual inspections - Backoulou et al. 2016 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Other factorsb - Visual inspections Visual inspections - Backoulou et al. 2013 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2011a, b Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Greenbug Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2015 Aerial – Manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Aerial – manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M Landsat-8, NASA 9 Wheat Wheat aphid Powdery mildew - Arthropod counts Damage assessments - Ma et al. 2019 aM = multispectral, H = hyperspectral. bIncl. damage caused by drought or poor fertilization. cIncl. damage caused by poor fertilization, germination, or tillage. Only studies involving at least one arthropod pest are included in this table. Studies mentioned in this table are also included in Tables 2–4; plant and arthropod species names are mentioned there. Open in new tab Table 5. Studies on hyperspectral and multispectral remote sensing to distinguish various biotic and abiotic stressors in crops Platform . Spectral resolutiona . Sensor details . No. of spectral bands . Plant common name . Stress 1 . Stress 2 . Stress 3 . Field observations 1 . Field observations 2 . Field observations 3 . References . Ground-based M MSR 16R, Cropscan Inc. 16 Wheat Russian wheat aphid Greenbug - Arthropod counts Arthropod counts - Yang et al. 2009b Ground-based H Pika II, Resonon 160 Corn Two-spotted spider mite Drought stress - Arthropod counts Different irrigation levels - Nansen et al. 2010, Nansen 2012 Ground-based H FieldSpec Pro FR, ASD 2,151 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Ground-based H FieldSpec Pro FR, ASD + GER 1500, Spectra Vista Corp. 2,151 + 512 Cotton Cotton aphid Two-spotted spider mite - Arthropod counts Arthropod counts or presence/ absence assessments - Reisig and Godfrey 2007 Ground-based H FieldSpec Handheld, ASD 512 Rice Brown planthopper Nitrogen stress - Arthropod counts Different fertilizer levels - Huang et al. 2015a Ground-based H Personal Spectrometer II, ASD 512 Wheat Russian wheat aphid Greenbug - Controlled infestations Controlled infestations - Riedell and Blackmer 1999 Ground-based H FieldSpec, FieldSpec Pro, or FieldSpec UV/VNIR, ASD 2,151 Wheat Wheat aphid Yellow rust Powdery mildew Damage assessments Damage assessments Damage assessments Huang et al. 2014; Yuan et al. 2014, 2017, Shi et al. 2017, Zhang et al. 2017 Aerial – manned aircraft M MS3100, Duncan Tech 3 Wheat Russian wheat aphid Greenbug - Visual inspections Visual inspections - Backoulou et al. 2016 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Other factorsb - Visual inspections Visual inspections - Backoulou et al. 2013 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2011a, b Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Greenbug Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2015 Aerial – Manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Aerial – manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M Landsat-8, NASA 9 Wheat Wheat aphid Powdery mildew - Arthropod counts Damage assessments - Ma et al. 2019 Platform . Spectral resolutiona . Sensor details . No. of spectral bands . Plant common name . Stress 1 . Stress 2 . Stress 3 . Field observations 1 . Field observations 2 . Field observations 3 . References . Ground-based M MSR 16R, Cropscan Inc. 16 Wheat Russian wheat aphid Greenbug - Arthropod counts Arthropod counts - Yang et al. 2009b Ground-based H Pika II, Resonon 160 Corn Two-spotted spider mite Drought stress - Arthropod counts Different irrigation levels - Nansen et al. 2010, Nansen 2012 Ground-based H FieldSpec Pro FR, ASD 2,151 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Ground-based H FieldSpec Pro FR, ASD + GER 1500, Spectra Vista Corp. 2,151 + 512 Cotton Cotton aphid Two-spotted spider mite - Arthropod counts Arthropod counts or presence/ absence assessments - Reisig and Godfrey 2007 Ground-based H FieldSpec Handheld, ASD 512 Rice Brown planthopper Nitrogen stress - Arthropod counts Different fertilizer levels - Huang et al. 2015a Ground-based H Personal Spectrometer II, ASD 512 Wheat Russian wheat aphid Greenbug - Controlled infestations Controlled infestations - Riedell and Blackmer 1999 Ground-based H FieldSpec, FieldSpec Pro, or FieldSpec UV/VNIR, ASD 2,151 Wheat Wheat aphid Yellow rust Powdery mildew Damage assessments Damage assessments Damage assessments Huang et al. 2014; Yuan et al. 2014, 2017, Shi et al. 2017, Zhang et al. 2017 Aerial – manned aircraft M MS3100, Duncan Tech 3 Wheat Russian wheat aphid Greenbug - Visual inspections Visual inspections - Backoulou et al. 2016 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Other factorsb - Visual inspections Visual inspections - Backoulou et al. 2013 Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Russian wheat aphid Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2011a, b Aerial – manned aircraft M MS3100, DuncanTech 3 Wheat Greenbug Drought stress Agronomic conditionsc Visual inspections Visual inspections Visual inspections Backoulou et al. 2015 Aerial – Manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Aerial – manned aircraft M + H SAMRSS + AVNIR, Opto-Knowledge Systems 4 + 60 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Spider mite - Arthropod counts Arthropod counts - Reisig and Godfrey 2006 Orbital M QuickBird, DigitalGlobe 3 Cotton Cotton aphid Nitrogen stress - Arthropod counts Different fertilizer levels, soil samples, plant nutrient uptake analysis - Reisig and Godfrey 2010 Orbital M Landsat-8, NASA 9 Wheat Wheat aphid Powdery mildew - Arthropod counts Damage assessments - Ma et al. 2019 aM = multispectral, H = hyperspectral. bIncl. damage caused by drought or poor fertilization. cIncl. damage caused by poor fertilization, germination, or tillage. Only studies involving at least one arthropod pest are included in this table. Studies mentioned in this table are also included in Tables 2–4; plant and arthropod species names are mentioned there. Open in new tab Actuation Drones for Precision Application of Pesticides While sensing drones could help detect pest hotspots, actuation drones could help control the pests at these hotspots. Pest hotspots could potentially be managed through variable rate application of pesticides. Aircrafts have been used for decades for pesticide sprays, but products are deposited over large areas, and a large amount is lost to drift (Pimentel 1995, Bird et al. 1996). This is a concern for neighboring terrestrial and aquatic ecosystems, as well as for human health (Damalas 2015). Major factors determining spray drift are droplet size (influenced by nozzle type and product formulation), weather conditions (e.g., wind speed and direction), and application method (e.g., spray height above the canopy) (Hofman and Solseng 2001, Al Heidary et al. 2014). Empirical and modeling studies showed that spray drift into non-target areas can be considerable (Woods et al. 2001, Sánchez-Bayo et al. 2002, Teske et al. 2002, Tsai et al. 2005, Al Heidary et al. 2014). Therefore, improved methods of pesticide application are highly needed (Lan et al. 2010), and there is potential for the use of drones in precision application of insecticides and miticides (Costa et al. 2012; Faiçal et al. 2014a,b, 2016, 2017; Brown and Giles 2018). Some of the aspects that give drones a competitive edge over manned crop dusters are their relative ease of deployment, reduction in operator exposure to pesticides, and potential reduction of spray drift (Faiçal et al. 2014b). Indeed, in Japan, where drones have been used in agriculture since the 1980s, drones are widely used to spray pesticides on rice, Oryza sativa L.. These drones are mostly heavier than 25 kg, but we discuss them here, as they are among the most widely used drones in pest management. Development of unmanned aerial vehicles for crop dusting started at the Japanese Agriculture, Forestry, and Fishery Aviation Association, an external organization of the Japanese Ministry of Agriculture, Forestry, and Fisheries. A prototype was completed in 1986 by Yamaha, a Japanese multinational corporation with a wide range of products and services, and the R-50 appeared on the market in 1987: the world’s first practical-use unmanned helicopter for pesticide applications, with a payload of 20 kg (Miyahara 1993, Sato 2003, Yamaha 2014a, Xiongkui et al. 2017). A few successors have launched since, with greater payload capacities and simplicity of use (Yamaha 2014b, 2016). In Japan alone, as of March 2016, about 2,800 unmanned helicopters are registered for operation, spraying more than a third of the country’s rice fields. The use of unmanned crop dusters has also spread to other crops, such as wheat, oats, and soybean, and the number of crops continues to expand (Yamaha 2016). Japanese unmanned crop dusters are also employed in South Korea (Xiongkui et al. 2017) and are currently being tested for spraying of pesticides in California vineyards (Bloss 2014, Giles and Billing 2015, Gillespie 2015). On a small but increasing scale, unmanned crop dusters are used in China, for crops such as rice, mango, and plantain (Zhou et al. 2013, Tang et al. 2016, Xiongkui et al. 2017, Lan and Chen 2018, Yang et al. 2018, Zhang et al. 2019). Novel types of unmanned crop dusters and/or novel spray rigs fitting commercially available drones are currently being developed in China (Ru et al. 2011, Xue et al. 2016, Xiongkui et al. 2017), South Korea (Shim et al. 2009), the United States (Huang et al. 2009), Ukraine (Pederi and Cheporniuk 2015, Yun et al. 2017), and Spain (Martinez-Guanter et al. 2019), among other places. Recently, smaller drone-based crop dusters appeared on the market, such as the DJI AGRAS MG-1S with a 10 kg payload (DJI 2019). A collaboration between Japan’s Saga University, Saga Prefectural Government Department of Agriculture, Forestry, and Fisheries, and OPTiM Corporation resulted in AgriDrone, a small drone that can pinpoint pesticide application. Interestingly, AgriDrone is also equipped with an UV bug zapper, recognizing and killing over 50 varieties of nocturnal agricultural pests at nighttime (OPTiM 2016). However, no peer-reviewed literature on this system has appeared since its announcement. Current research focuses on improved spray coverage, to enable large-scale adoption of drones for application of pesticides (Qin et al. 2016, Wang et al. 2019a, Wang et al. 2019b). In combination with precision monitoring, precision application of pesticides could reduce the overall number of sprays, contributing to reduced pesticide use and decreased development of resistance, as well as increased presence of natural enemies (Midgarden et al. 1997). Actuation Drones for Precision Releases of Natural Enemies Biological control is a potential sustainable alternative to pesticide use. It is the use of a population of one organism to decrease the population of another, unwanted, organism (Van Lenteren et al. 2018). Biological control organisms include, but are not limited to, parasitoids, predators, entomopathogenic nematodes, fungi, bacteria, and viruses. A large variety is commercially available. Drones may be a particularly useful tool for augmentative biological control, which relies on the large-scale release of natural enemies for immediate control of pests (Van Lenteren et al. 2018). They could distribute the natural enemies in the exact locations where they are needed, which may increase biocontrol agent efficacy and reduce distribution costs. Some natural enemies, such as insect-killing fungi and nematodes, can be applied with conventional spray application equipment (Shah and Pell 2003, Shapiro-Ilan et al. 2012). Therefore, these biocontrol agents could potentially be applied by drones as described above for pesticides (Berner and Chojnacki 2017). However, application of other natural enemies is often costly and time-consuming. For example, the predatory mite Phytoseiulus persimilis Athias-Henriot (Acari: Phytoseiidae), an important natural enemy of the worldwide pest two-spotted spider mite, is available in bottles mixed with the mineral substrate vermiculite, and the recommended way of dispersal is by sprinkling contents onto individual plants (e.g., Koppert 2017a, Biobest 2018). Phytoseiulus persimilis has such a high level of specialization that populations succumb when no prey is present (McMurtry and Croft 1997, Cakmak et al. 2006, Gerson and Weintraub 2007, Dara 2014). Various mechanical distribution systems have been developed to facilitate predator dispersal, such as the Mini-Airbug, a handheld appliance with a fan (Koppert 2017b), as well as other devices (Giles et al. 1995, Casey and Parrella 2005, Opit et al. 2005). Growers in Brazil are known to use dispensers attached to motorbikes (Parra 2014, Agronomic Nordeste 2015), but this could potentially damage the crop. Release of natural enemies by aircraft was proposed in the 1980s (Herren et al. 1987, Pickett et al. 1987), but small drones would offer myriad possibilities. Coverage of larger areas compared to manual distribution, reducing application costs per acre, potentially increases the use of natural enemies in favor of pesticide sprays. Development of drone-mounted dispensers has mainly focused on two types of natural enemies: predatory mites such as the above-mentioned P. persimilis, and parasitoid wasps such as the egg-parasitoid Trichogramma spp. (Hymenoptera: Trichogrammatidae). To combat two-spotted spider mite, an important pest of a large number of crops worldwide, a California-based company is offering services to distribute predatory mites using drones, on crops such as strawberry (Parabug 2019). An Australia-based company also uses drones to distribute predatory mites on strawberry crops (Drone Agriculture 2018). At the University of Queensland in Australia, a drone-mounted device is being developed to distribute predatory mites in corn (Pearl 2015). At the University of California Davis, Dr. Z. Kong and Dr. C. Nansen, in collaboration with aerospace engineering students, have developed a platform for drone-based distribution of predatory mites, BugBot (Teske et al. 2019) (Fig. 5). They are currently testing the prototype and accompanying software, to optimize natural enemy releases. We propose that collaboration between growers, agricultural scientists, aerospace engineers, and software programmers is key in developing a product that is effective and user-friendly. Fig. 5. Open in new tabDownload slide Prototype of BugBot predatory mite dispenser. BugBot, developed by mechanical and aerospace engineering students at the University of California Davis, is a drone-mounted dispenser that can distribute predatory mites, important biological control agents of spider mites. In the picture, the BugBot dispenses vermiculite, the mineral substrate the predators can be obtained in. Fig. 5. Open in new tabDownload slide Prototype of BugBot predatory mite dispenser. BugBot, developed by mechanical and aerospace engineering students at the University of California Davis, is a drone-mounted dispenser that can distribute predatory mites, important biological control agents of spider mites. In the picture, the BugBot dispenses vermiculite, the mineral substrate the predators can be obtained in. Trichogramma spp. parasitoids are important biocontrol agents of European corn borer [Ostrinia nubilalis Hübner (Lepidoptera: Crambidae)], a major pest of sweet corn in the United States and Europe (Smith 1996). Various companies and research institutes all over the world have started Trichogramma drone applications, including Austria, Germany, France, Italy, and Canada (e.g., Chaussé et al. 2017, Airborne Robotics 2018). Drone-released Trichogramma parasitoids are also deployed in China for control of pests in sugarcane (Saccharum spp.) (Li et al. 2013, Yang et al. 2018). In Brazil, drone applications of Trichogramma spp., as well as the parasitoid Cotesia flavipes Cameron (Hymenoptera: Braconidae), are employed to combat the sugarcane borer [Diatraea saccharalis Fabricius (Lepidoptera: Crambidae)] in sugarcane. Trichogramma spp. are also employed against various other lepidopteran pests in other crops (Parra 2014, Rangel 2016, Xfly Brasil 2017). While we did not address pest management in forestry settings in this review, a recent report by Martel et al. (2018) deserves to be mentioned, as it is the first to compare drone release and ground release of natural enemies. The report evaluated the efficacy of Trichogramma spp. to combat spruce budworm [Choristoneura fumiferana Clemens (Lepidoptera: Tortricidae)], an important pest of fir and spruce trees in Canada and the United States. Drone releases, using Trichogramma-parasitized host eggs mixed with vermiculite, were compared to ground releases, using commercially available cards containing parasitized eggs of Mediterranean flour moth [Ephestia kuehniella Zeller (Lepidoptera: Pyralidae)]. Data were collected in two locations in Quebec, Canada. In one of these locations, drone release resulted in similar spruce budworm egg parasitism rates as ground release of natural enemies. Results for the other location were inconclusive, as egg parasitism rates were negligible. Drone releases were reportedly faster than ground releases of natural enemies. Although more studies are necessary, these preliminary results show the high potential of drone-based Trichogramma distribution in forests, especially on small scales, and in conditions under which insecticide applications are not appropriate (Martel et al. 2018). It is important to perform similar studies in field crops and orchards, to evaluate the efficacy of drone-released natural enemies. Other types of natural enemies can be drone-applied as well, such as green lacewing, [Chrysoperla spp. (Neuroptera: Chrysopidae)] and minute pirate bug [Orius insidiosus Say (Hemiptera: Anthocoridae)] to control aphids and thrips, and mealybug destroyer [Cryptolaemus montrouzieri Mulsant (Coleoptera: Coccinellidae)] to control mealybugs (Parabug 2019). Researchers at the University of Southern Denmark, in collaboration with Aarhus University, are currently developing a dispensing mechanism for ladybirds and other important natural enemies of aphids (SDU 2018). EWH BioProduction, a producer of beneficial organisms (EWH BioProduction 2019), is also involved in this EcoDrone project, as well as Ecobotix, a company offering drone-based services, which is developing a separate solution for dispensing natural enemies (Ecobotix 2018). Drone-based dispensers could be adapted or newly developed for other types of beneficial arthropods as well. Thus far, little to no peer-reviewed research exists on the efficacy of these operations. Therefore, this is a call for additional research. It is of utmost importance to verify that natural enemies distributed by drones are not damaged during transport and distribution and are still effective as biological control agents. Also, it is necessary to develop hardware and software mechanisms that can precisely distribute the natural enemies in different weather conditions, particularly considering that wind is a crucial factor for the distribution. Individual drone-mounted dispensers all use different technologies, which could be compared to optimize natural enemy distribution. This could pave the way for larger-scale operations of this promising resource. Novel Uses for Drones in Precision Pest Management Pest Outbreak Prevention Sensing and actuation drones could potentially contribute to the prevention of pest outbreaks. Plants exposed to abiotic stressors, such as drought and nutrient deficiencies, are often more susceptible to biotic stressors. This holds true for a large variety of arthropod pests, such as spider mites (Garman and Kennedy 1949, Rodriguez and Neiswander 1949, Rodriguez 1951, Perring et al. 1986, Stiefel et al. 1992, Machado et al. 2000, Abdel-Galil et al. 2007, Chen et al. 2007, Nansen et al. 2013, Ximénez-Embún et al. 2017), aphids (Myers and Gratton 2006, Walter and Difonzo 2007, Lacoste et al. 2015), and lepidopteran larvae (Gutbrodt et al. 2011, 2012; Grinnan et al. 2013; Weldegergis et al. 2015). Due to this well-established association between abiotic stressors and risk of arthropod pest outbreaks, it may be argued that precision application of abiotic stress relief, such as application of water and fertilizer, represents a meaningful approach to reducing the risk of outbreaks by some arthropod pests (Nansen et al. 2013, West and Nansen 2014). Indeed, pest management focus could shift from being based mainly on responsive insecticide applications to a more preventative approach in which maintaining crop health is the main focus (Culliney and Pimentel 1986, Altieri and Nicholls 2003, Zehnder et al. 2007, Amtmann et al. 2008, West and Nansen 2014). Use of sensing and actuation drones could contribute to this shift, by assessing plant stress status, and preventative applications of water and fertilizers. To the best of our knowledge, drones have thus far not been deployed for precision irrigation purposes, and although drones are on the market that advertise the capacity to apply liquid or granular fertilizers, there is no peer-reviewed literature on their use. Many current spray tractors contain options for variable rate applications of nutrients, for an adequate response to deficiencies detected with remote sensing (Raun et al. 2002). However, there would be myriad opportunities for use of drones in this respect, due to their maneuverability and capacity to treat small areas. Reducing Pest Populations: Sterile Insect Technique and Mating Disruption A potential new area for use of drones in pest management is the release of sterile insects. Codling moth [Cydia pomonella L. (Lepidoptera: Tortricidae)] is a major problem in apple orchards (Malus domestica Borkh.) (Judd and Gardiner 2005), and pilot programs to release sterile insects with drones have been successful in controlling codling moth populations in New Zealand, Canada, and the United States (DuPont 2018, M3 Consulting Group 2018, Seymour 2018, Timewell 2018). Furthermore, pilot programs for control of pink bollworm [Pectinophora gossypiella Saunders (Lepidoptera: Gelechiidae)] in cotton, and Mexican fruit fly [Anastrepha ludens Loew (Diptera: Tephritidae)] in citrus, with drone-released sterile insects proved effective for control of these pests in the United States (Rosenthal 2017). Similarly, false codling moth [Thaumatotibia leucotreta Meyrick (Lepidoptera: Tortricidae)] could successfully be controlled in citrus orchards in South Africa (FlyH2 Aerospace 2018). The sterile insect technique (SIT) produces sterile or partially sterile insects through irradiation. After mating with wild insects, there is either no offspring, or the resulting offspring is sterile, resulting in reduced pest populations. SIT is environmentally friendly, species-specific, and compatible with other management methods such as biological control, making it an important IPM tool (Simmons et al. 2010). Drone release of the sterile insects may be cheaper and faster than ground release, which occurs for instance by means of all-terrain vehicles (ATVs), or release by manned aircraft (Tan and Tan 2013). For sterile codling moth, drone-dispersal may also improve moth performance. Drones release the moths above the canopy whereas ATVs release them on the orchard floor. Codling moth prefer to mate in the upper one-third of the canopy, thus drone release may facilitate the moths reaching their preferred habitat, while minimizing biotic and abiotic mortality factors. Irradiated moths must be kept chilled during transportation prior to orchard dispersal to prevent damage and scale loss. An optimized delivery system from the rearing facility to the orchard may increase the sterile moths’ effectiveness in mating with wild moths (DuPont 2018, Dr. E. Beers, personal communication). Therefore, drone releases may make SIT more widely available. Drones could also be deployed to place mating disruptors such as SPLAT (specialized pheromone & lure application technology) in commercial fields (FlyH2 Aerospace 2018). SPLAT is an inert matrix which can be infused with pheromones and/or pesticides and is applied as dollops (ISCA 2019a, b). Mating disruption relies on the release of pheromones, which interferes with mate finding (Miller and Gut 2015), while attract-and-kill involves an attractant and a killing agent (Gregg et al. 2018). A combination of these methods effectively control various pests in a number of cropping systems, including blueberry (Vaccinium corymbosum L.) and cranberry (Rodriguez-Saona et al. 2010, Steffan et al. 2017). Researchers from the University of Wisconsin are currently developing a drone release mechanism for SPLAT, to improve IPM practices in cranberry (Miller 2015, Chasen and Steffan 2017, Seely 2018). Pest Population Monitoring Drones could also be used to track populations of mobile insects that can be equipped with transponders, such as locusts (Tahir and Brooker 2009). A recent paper by Stumph et al. (2019) described the use of drones equipped with a UV light source and a video camera to detect fluorescent-marked insects. Brown marmorated stink bugs [Halyomorpha halys Stål (Hemiptera: Pentatomidae)], 13–16 mm long, were coated in red fluorescent powder, and placed in a grass field. Drone data were obtained at night, and specific software was developed to visualize individual insects. This system provides a relatively fast alternative for manual, time-consuming, mark-release-recapture studies. Although insects still need to be coated initially, the method eliminates the need to physically recapture the insects. Also, it removes the need for destructive sampling, so that insects could potentially be sampled over a longer time period. Thus, use of this novel, drone-based system could improve efficiency and cost-effectiveness of mark-release-recapture studies of insect migration (Stumph et al. 2019). Furthermore, drones could be used to collect pest specimens for monitoring (Shields and Testa 1999, Kim et al. 2018), or to survey for pests, such as Asian longhorned beetles [Anoplophora glabripennis Motschulsky (Coleoptera: Cerambycidae)], in tall trees, assisting tree climbers (Rosenthal 2017). A recent review has even suggested the use of drones for collection of plant volatiles (Gonzalez et al. 2018). Indeed, plant volatiles induced in response to herbivory could indicate the presence of specific pests (Turlings and Erb 2018, De Lange et al. 2019), and drone-based volatile collections have been deployed for air quality measurements (Villa et al. 2016). Development of novel sensors and technology will undoubtedly open the door to various other uses of drones in agricultural pest management. Challenges and Opportunities Major challenges for the use of drones in precision agriculture are the costs of drones and associated sensors and material, limited flight time and payload, and continuously changing regulations. For a more comprehensive review of challenges and opportunities of drones in precision agriculture and environmental studies, two fields that share similar uses of drones, see Hardin and Jensen (2011), Zhang and Kovacs (2012), Whitehead and Hugenholtz (2014), and Whitehead et al. (2014). We here focus specifically on the technical challenges for the use of drones in precision pest management, and highlight recent changes in regulations. Costs A major challenge for the use of drones in precision pest management is the initial steep costs of the material: the drone itself, the various sensors or application technologies, mounting equipment, and analysis software. Although costs are decreasing with improving technology, sums are still relatively high. In 2017, costs of a fixed-wing drone with hyperspectral sensor were estimated at €120,000 ($144,000), while costs of a multi-rotor drone with a multispectral sensor were estimated at €10,000 ($12,000) (Pádua et al. 2017). Therefore, various companies are offering drone-related services, such as renting out drones with remote sensing equipment (e.g., Blue Skies 2019) or offering predator dispersal services (e.g., Parabug 2019). Also, consulting companies offer remote sensing and data analysis services for a reasonable fee, even combined with other agriculture-related services, to provide one platform for efficient record-keeping and planning (e.g., UAV-IQ 2018). Data Collection, Analysis, and Interpretation Concerning sensing drones, repeatability of remote sensing data is a recurring issue. Canopy reflectance varies depending on solar angle, cloud coverage, and various other factors. Therefore, it is difficult to compare data obtained on a specific day with data obtained the next day, even the next hour. Novel methods for calibration and processing of drone-based remote sensing data are continuously being developed (Bourgeon et al. 2016, Singh and Nansen 2017, Aasen et al. 2018). Improved repeatability will render these data more useful for precision detection of pest problems. Data analysis is also an important challenge. Each mission with a hyperspectral sensor typically results in multiple terabytes of data, which must be properly stored, processed with specific software, and analyzed by experts with years of experience. As a result, there is an important time lag between data collection and the visibility of results. Processing of multispectral data is currently much faster than processing of hyperspectral data, but the results are less precise in terms of detection of pest problems (Yang et al. 2009a). Ultimately, automation of data analysis will improve the usability of detailed hyperspectral datasets by growers directly, leading to a timelier detection and possible response to the discovery of pest hotspots. Also, automated data analysis will facilitate communication between sensing and actuation drones, so that an actuation drone can immediately be deployed to provide solutions. Or, a single drone could function simultaneously as sensor and actuator, and directly apply solutions where necessary (Fig. 1). Concerning actuation drones, peer-reviewed research has just started to emerge, with many challenges to be overcome. One major challenge is that, in order to develop an effective actuation drone system, knowledge and expertise from multiple fields must be integrated. First, knowledge from agricultural scientists will be needed to answer research questions such as where, when, and how much of the solutions (e.g., pesticides and natural enemies) should be applied in an agricultural field. Second, engineers and software developers will need to convert such knowledge into the design of hardware and software components for the effective and efficient distribution of the solutions. Another technical challenge is the automation of the distribution of solutions. Considering the complicated and varied field and weather conditions, preferentially, users should not be asked to set up all the software parameters by themselves. Instead, the drone should be able to compute and implement the optimal distribution strategy automatically (potentially being given a digital map built by sensing drones). (Fig. 1) Flight Time and Payload Concerning both sensing and actuation drones, flight time and payload are among the most limiting factors for use of drones in agriculture. Although individual drones can have payloads of 24 kg and up (Yamaha 2016), it would be challenging, though not impossible to develop a drone that can both detect pest hotspots and apply solutions. Indeed, the above-mentioned AgriDrone can both detect pest hot spots and apply localized solutions (OPTiM 2016). However, to cover large areas, using a network of communicating drones, or swarm, may eventually be most efficient (Stark et al. 2013a, Faiçal et al. 2014a, Gonzalez-de-Santos et al. 2017). Ultimately, one or multiple sensing drones detecting pest hotspots will communicate with one or multiple actuation drones dispensing biological control organisms or agrochemicals exactly where needed; they can also autonomously fly back to their base stations to recharge, without further human intervention. Establishing drone swarms is an active research area in the drone community (Bertuccelli et al. 2009, Alejo et al. 2014, Ponda et al. 2015). However, how to translate these techniques into the pest management application domain is still an open question. Adverse Weather Conditions and Other Environmental Factors Adverse weather conditions could limit sensing and actuation drone activity. Most drones have an optimal operating temperature range. Strong wind could interfere with obtaining aerial remote sensing data, as well as with pesticide or biocontrol dispersal. Ideally, remote sensing measurements should be taken all under the same solar and sensor angle geometry, to avoid differences due to the effect that natural surfaces scatter radiation unequally into all directions (Weyermann et al. 2014). Data acquisition with a clear, cloudless sky, at solar noon reduces shadow influences as well as variations between measurements due to changing light intensity resulting from cloud cover (Souza et al. 2010). However, these conditions cannot be easily obtained in farms all over the world. Clouds and fog limit drone flights, and it is not recommended to fly a drone in rain or snow conditions, or during thunderstorms. Other environmental factors limiting drone activity are differences in elevation within fields or orchards, and presence of wildlife, such as birds (Park et al. 2012). Rules and Regulations In the United States, Federal Aviation Regulations (FARs) are in place for the commercial and research use of drones, prescribed by the FAA. Until 2016, a manned aircraft pilot license was necessary to fly a drone, which is costly to obtain and maintain. As of August 2016, a less stringent remote pilot license became available to operate small drones, which made commercial drone use much more readily available (FAA 2016). However, the regulations are regularly updated, which requires that pilots keep continuous track of current regulations. A few basic rules in the United States include that the pilot in command must keep a visual line of sight (VLOS) on the drone at all times. Consequently, flying is only allowed at daylight hours. Drones must fly at an altitude at or below 400 feet (122 m), at a speed at or below 100 mph (161 km/h). They are not allowed to fly over people that are not involved in the specific drone operation, and must always yield right of way to larger aircraft, including manned aircraft. Waivers from these regulations, for instance to fly at nighttime, can be requested through the FAA. Importantly, the pilot in command must perform a pre-flight check before each flight, to ascertain that the drone is in good condition for safe operation (FAA 2018b). In the United States, drones for both commercial and private use must be registered through the FAA. Regulations for operating and registering a drone may vary in different countries, so international collaborators must make sure to follow the proper rules (Cracknell 2017, Stöcker et al. 2017). In Brazil, where drones are regularly used in precision agriculture (Jorge et al. 2014, Parra 2014), the use of drones for civil and agricultural means was regulated as recently as May 2017 by the National Agency of Civil Aviation (ANAC) (Agência Nacional de Aviação Civil 2017). Ultimately, when drones become more mainstream, general rules may become more standardized. Communication With Growers Importantly, increased use of drones in commercial agricultural operations will not happen without adoption of the technology by growers, and they will only adopt technology that is proven to work, cost-effective, and compatible with established practices (Aubert et al. 2012, Pierpaoli et al. 2013). Extensive communication and collaboration between scientists, industry professionals, and commercial growers is needed to provide the best performing technology that tailors to growers’ needs (Larson et al. 2008, Lindblom et al. 2017). Extension agents, dedicated to the translation of scientific research to practical applications, may facilitate these connections, through training and dialogue. Conclusion Drones are becoming increasingly adopted as part of precision agriculture and IPM. Drones with remote sensing equipment (sensors) are deployed to monitor crop health, map out variability in crop performance, and detect outbreaks of pests. They could serve as decision support tools, as early detection and response to suboptimal abiotic conditions may prevent large pest outbreaks. When outbreaks do occur, different drones (actuators) could be deployed to deliver swift solutions to identified pest hotspots. Automating pesticide applications and/or release of biological control organisms, through communication between sensing and actuation drones, is the future. This approach requires multi-disciplinary research in which engineers, ecologists, and agronomists are converging, with enormous commercial potential. Acknowledgments We thank April Teske and Kevin Goding for critical comments on an earlier version of this manuscript. Thanks to Eli Borrego for help creating Fig. 2. We thank the commercial growers who made their fields available for research activities. F.H.I.F. is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) - Finance Code 001. Z.K. is supported by the California Department of Pesticide Regulation (project 18-PML-R004). E.S.d.L. is supported by Western Sustainable Agriculture Research and Education (project SW17-060, http://www.westernsare.org/). This study was also supported by the American Floral Endowment, the Gloeckner Foundation, and United States Department of Agriculture, Agricultural Research Service (USDA ARS) Floriculture and Nursery Research Initiative. References Cited Aasen , H. , and A. Bolten . 2018 . Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to applicaton . Remote Sens. Environ . 205 : 374 – 389 . Google Scholar Crossref Search ADS WorldCat Aasen , H. , E. Honkavaara , A. Lucieer , and P. Zarco-Tejada . 2018 . Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows . Remote Sens . 10 : 1091 . Google Scholar Crossref Search ADS WorldCat Abdel-Galil , F. A. , M. A. M. Amro , and A. S. H. Abdel-Moniem . 2007 . Effect of drought stress on the incidence of certain arthropod pests and predators inhabiting cowpea plantations . Arch. Phytopathology Plant. Protect . 40 : 207 – 214 . Google Scholar Crossref Search ADS WorldCat Abdel-Rahman , E. M. , M. Van den Berg , M. J. Way , and F. B. Ahmed . 2009 . Hand-held spectrometry for estimating thrips (Fulmekiola serrata) incidence in sugarcane, pp. 268 – 271 . In IEEE International Geoscience and Remote Sensing Symposium , 12–17 July 2009 , Cape Town, South Africa . Abdel-Rahman , E. M. , F. B. Ahmed , M. van den Berg , and M. J. Way . 2010 . Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection . Int. J. Remote Sens . 31 : 4199 – 4216 . Google Scholar Crossref Search ADS WorldCat Abdel-Rahman , E. M. , M. Way , F. Ahmed , R. Ismail , and E. Adam . 2013 . Estimation of thrips (Fulmekiola serrata Kobus) density in sugarcane using leaf-level hyperspectral data . S. Afr. J. Plant & Soil . 30 : 91 – 96 . Google Scholar Crossref Search ADS WorldCat Abdel-Rahman , E. M. , T. Landmann , R. Kyalo , G. Ong’amo , S. Mwalusepo , S. Sulieman , and B. Le Ru . 2017 . Predicting stem borer density in maize using RapidEye data and generalized linear models . Int. J. Appl. Earth Obs. Geoinf . 57 : 61 – 74 . Google Scholar Crossref Search ADS WorldCat ABI Research . 2018 . Drones in agriculture: undeniable value and plenty of growth, but not the explosion others predict . Available from https://www.abiresearch.com/press/drones-agriculture-undeniable-value-and-plenty-gro/ Agência Nacional de Aviação Civil . 2017 . Regas da ANAC para uso de drones entram em vigor . Available from http://www.anac.gov.br/noticias/2017/regras-da-anac-para-uso-de-drones-entram-em-vigor/release_drone.pdf Agronomic Nordeste . 2015 . Trichobug (Trichogramma) . Available from http://agromicnordeste.com.br/produtos Airborne Robotics . 2018 . Agriculture & forestry . Available from https://www.air6systems.com/portfolio/agriculture-forestry/ Alejo , D. , J. Cobano , G. Heredia , and A. Ollero . 2014 . Optimal reciprocal collision avoidance with mobile and static obstacles for multi-UAV systems, pp. 1259 – 1266 . In IEEE International Conference on Unmanned Aircraft Systems (ICUAS) , 27–30 May 2014 , Orlando, FL . Al Heidary , M. , J. P. Douzals , C. Sinfort , and A. Vallet . 2014 . Influence of spray characteristics on potential spray drift of field crop sprayers: a literature review . Crop Prot . 63 : 120 – 130 . Google Scholar Crossref Search ADS WorldCat Altieri , M. A. , and C. I. Nicholls . 2003 . Soil fertility management and insect pests: harmonizing soil and plant health in agroecosystems . Soil Tillage Res . 72 : 203 – 211 . Google Scholar Crossref Search ADS WorldCat Alves , T. M. , I. V. Macrae , and R. L. Koch . 2015 . Soybean aphid (Hemiptera: Aphididae) affects soybean spectral reflectance . J. Econ. Entomol . 108 : 2655 – 2664 . Google Scholar Crossref Search ADS PubMed WorldCat Alves , T. M. , R. D. Moon , I. V. MacRae , and R. L. Koch . 2019 . Optimizing band selection for spectral detection of Aphis glycines Matsumura in soybean . Pest Manag. Sci . 75 : 942 – 949 . Google Scholar Crossref Search ADS PubMed WorldCat Amtmann , A. , S. Troufflard , and P. Armengaud . 2008 . The effect of potassium nutrition on pest and disease resistance in plants . Physiol. Plant 133 : 682 – 691 . Google Scholar Crossref Search ADS PubMed WorldCat Anderson , K. , and K. J. Gaston . 2013 . Lightweight unmanned aerial vehicles will revolutionize spatial ecology . Front. Ecol. Environ . 11 : 138 – 146 . Google Scholar Crossref Search ADS WorldCat Aubert , B. A. , A. Schroeder , and J. Grimaudo . 2012 . IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology . Decis. Support Syst . 54 : 510 – 520 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , K. Giles , M. Phoofolo , and V. Catana . 2011a . Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia . Comput. Electron. Agric . 75 : 64 – 70 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , K. Giles , M. Phoofolo , V. Catana , M. Mirik , and J. Michels . 2011b . Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors . Comput. Electron. Agric . 78 : 123 – 129 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , K. L. Giles , and M. N. Rao . 2013 . Differentiating stress to wheat fields induced by Diuraphis noxia from other stress causing factors . Comput. Electron. Agric . 90 : 47 – 53 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , K. L. Giles , and M. Mirik . 2015 . Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes . Comput. Electron. Agric . 115 : 34 – 39 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , and K. L. Giles . 2016 . Using multispectral imagery to compare the spatial pattern of injury to wheat caused by Russian wheat aphid and greenbug . Southwest. Entomol . 41 : 1 – 8 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. , N. Elliott , K. Giles , T. Alves , M. Brewer , and M. Starek . 2018a . Using multispectral imagery to map spatially variable sugarcane aphid infestations in sorghum . Southwest. Entomol . 43 : 37 – 44 . Google Scholar Crossref Search ADS WorldCat Backoulou , G. F. , N. C. Elliott , K. L. Giles , M. J. Brewer , and M. Starek . 2018b . Detecting change in a sorghum field infested by sugarcane aphid . Southwest. Entomol . 43 : 823 – 832 . Google Scholar Crossref Search ADS WorldCat Barbedo , J. G. A . 2019 . A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses . Drones 3 : 40 . Google Scholar Crossref Search ADS WorldCat Berner , B. , and J. Chojnacki . 2017 . Influence of the air stream produced by the drone on the sedimentation of the sprayed liquid that contains entomopathogenic nematodes . J. Res. Appl. Agric. Eng . 62 : 26 – 29 . OpenURL Placeholder Text WorldCat Bertuccelli , L. , H.-L. Choi , P. Cho , and J. How . 2009 . Real-time multi-UAV task assignment in dynamic and uncertain environments, pp. 1 – 16 . In AIAA Guidance, Navigation, and Control Conference , 10–13 August 2009 , Chicago, IL . Bhattarai , G. P. , R. B. Schmid , and B. P. McCornack . 2019 . Remote sensing data to detect hessian fly infestation in commercial wheat fields . Sci. Rep . 9 : 6109 . Google Scholar Crossref Search ADS PubMed WorldCat Biobest . 2019 . Drones bundled with cameras or sensors . Available from https://www.blueskiesdronerental.com/product-category/rentals/drone-bundles/ Bird , S. L. , D. M. Esterly , and S. G. Perry . 1996 . Off-target deposition of pesticides from agricultural aerial spray applications . J. Environ. Qual . 25 : 1095 – 1104 . Google Scholar Crossref Search ADS WorldCat Bloss , R . 2014 . Robot innovation brings to agriculture efficiency, safety, labor savings and accurary by plowing, milking, harvesting, crop tending/picking and monitoring . Ind. Rob . 41 : 493 – 499 . Google Scholar Crossref Search ADS WorldCat Blue Skies . 2019 . Drones bundled with cameras or sensors . Available from https://www.blueskiesdronerental.com/product-category/rentals/drone-bundles/ Bourgeon , M.-A. , J.-N. Paoli , G. Jones , S. Villette , and C. Gée . 2016 . Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage . Comput. Electron. Agric . 123 : 184 – 194 . Google Scholar Crossref Search ADS WorldCat Brown , C. R. , and D. K. Giles . 2018 . Measurement of pesticide drift from unmanned aerial vehicle application to a vineyard . Trans. ASABE 61 : 1539 – 1546 . Google Scholar Crossref Search ADS WorldCat Cakmak , I. , A. Janssen , and M. W. Sabelis . 2006 . Intraguild interactions between the predatory mites Neoseiulus californicus and Phytoseiulus persimilis . Exp. Appl. Acarol 38 : 33 – 46 . Google Scholar Crossref Search ADS PubMed WorldCat Calderón , R. , J. A. Navas-Cortés , C. Lucena , and P. J. Zarco-Tejada . 2013 . High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices . Remote Sens. Environ . 139 : 231 – 245 . Google Scholar Crossref Search ADS WorldCat Carrière , Y. , P. C. Ellsworth , P. Dutilleul , C. Ellers-Kirk , V. Barkley , and L. Antilla . 2006 . A GIS-based approach for areawide pest management: the scales of Lygus hesperus movements to cotton from alfalfa, weeds, and cotton . Entomol. Exp. Appl . 118 : 203 – 210 . Google Scholar Crossref Search ADS WorldCat Carroll , M. W. , J. A. Glaser , R. L. Hellmich , T. E. Hunt , T. W. Sappington , D. Calvin , K. Copenhaver , and J. Fridgen . 2008 . Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots . J. Econ. Entomol . 101 : 1614 – 1623 . Google Scholar Crossref Search ADS PubMed WorldCat Carter , G. A. , and A. K. Knapp . 2001 . Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration . Am. J. Bot . 88 : 677 – 684 . Google Scholar Crossref Search ADS PubMed WorldCat Casey , C. A. , and M. P. Parrella . 2005 . Evaluation of a mechanical dispenser and interplant bridges on the dispersal and efficacy of the predator, Phytoseiulus persimilis (Acari: Phytoseiidae) in greenhouse cut roses . Biol. Control 32 : 130 – 136 . Google Scholar Crossref Search ADS WorldCat Chasen , E. , and S. Steffan . 2017 . Update on mating disruption in cranberries: the story of SPLAT® . Proceedings of the Wisconsin Cranberry School 25 : 23 – 25 . Available from https://fruit.wisc.edu/wp-content/uploads/sites/36/2017/03/2017-Cranberry-School-Proceedings-Final.pdf OpenURL Placeholder Text WorldCat Chaussé , S. , L. Jochems-Tanguay , T. Boislard , D. Cormier , and J. Boisclair . 2017 . Lâchers de trichogrammes par drones, une nouvelle approche pour lutter contre la pyralide du maïs dans le maïs sucré de transformation . In Congrès Annuel de la Société d’Entomologie du Québec , 23–24 November 2017 , Longueuil, Canada . Available from https://www.irda.qc.ca/assets/documents/Publications/documents/simon_chausse_seq2017.pdf Chen , Y. , G. P. Opit , V. M. Jonas , K. A. Williams , J. R. Nechols , and D. C. Margolies . 2007 . Twospotted spider mite population level, distribution, and damage on ivy geranium in response to different nitrogen and phosphorus fertilization regimes . J. Econ. Entomol . 100 : 1821 – 1830 . Google Scholar Crossref Search ADS PubMed WorldCat Chen , T. , R. Zeng , W. Guo , X. Hou , Y. Lan , and L. Zhang . 2018 . Detection of stress in cotton (Gossypium hirsutum L.) caused by aphids using leaf level hyperspectral measurements . Sensors 18 : 2798 . Google Scholar Crossref Search ADS WorldCat Congalton , R. G . 1991 . A review of assessing the accuracy of classifications of remotely sensed data . Remote Sens. Environ . 37 : 35 – 46 . Google Scholar Crossref Search ADS WorldCat Costa , F. G. , J. Ueyama , T. Braun , G. Pessin , F. S. Osório , and P. A. Vargas . 2012 . The use of unmanned aerial vehicles and wireless sensor network in agricultural applications, pp. 5045 – 5048 . In IEEE International Geoscience and Remote Sensing Symposium , 22–27 July 2012 , Munich, Germany . Cracknell , A. P . 2017 . UAVs: regulations and law enforcement . Int. J. Remote Sens . 38 : 3054 – 3067 . Google Scholar Crossref Search ADS WorldCat Culliney , T. W. , and D. Pimentel . 1986 . Ecological effects of organic agricultural practices on insect populations . Agric. Ecosyst. Environ . 15 : 253 – 266 . Google Scholar Crossref Search ADS WorldCat Dalamagkidis , K . 2015 . Classification of UAVs, pp. 83 – 91 . In K. P. Valavanis and G. J. Vachtsevanos (eds.), Handbook of unmanned aerial vehicles . Springer , Dordrecht, Netherlands . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Damalas , C. A . 2015 . Pesticide drift: seeking reliable environmental indicators of exposure assessment . In R. H. Armon and O. Hönninen (eds.), Environmental indicators . Springer , Dordrecht, Netherlands . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Dara , S. K . 2014 . Predatory mites for managing spider mites on strawberries . UC ANR eJournal of Entomology and Biologicals . Available from https://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=14065 OpenURL Placeholder Text WorldCat Dara , S. K . 2019 . The new integrated pest management paradigm for the modern age . J. Int. Pest Manag . 10 : 12 . OpenURL Placeholder Text WorldCat Das , P. K. , K. K. Choudhary , B. Laxman , S. V. C. K. Rao , and M. V. R. Seshasai . 2014 . A modified linear extrapolation approach towards red edge position detection and stress monitoring of wheat crop using hyperspectral data . Int. J. Remote Sens . 35 : 1432 – 1449 . Google Scholar Crossref Search ADS WorldCat Dash , J. P. , D. Pont , R. Brownlie , A. Dunningham , M. Watt , and G. Pearse . 2016 . Remote sensing for precision forestry . NZ J. Forestry 60 : 15 – 24 . OpenURL Placeholder Text WorldCat Dash , J. P. , G. D. Pearse , and M. S. Watt . 2018 . UAV multispectral imagery can complement satellite data for monitoring forest health . Remote Sens . 10 : 1216 . Google Scholar Crossref Search ADS WorldCat Daughtry , C. S. T. , C. L. Walthall , M. S. Kim , E. Brown de Colstoun , and J. E. McMurtrey III . 2000 . Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance . Remote Sens. Environ . 74 : 229 – 239 . Google Scholar Crossref Search ADS WorldCat De Lange , E. S. , J. Salamanca , J. Polashock , and C. Rodriguez-Saona . 2019 . Genotypic variation and phenotypic plasticity in gene expression and emissions of herbivore-induced volatiles, and their potential tritrophic implications, in cranberries . J. Chem. Ecol . 45 : 298 – 312 . Google Scholar Crossref Search ADS PubMed WorldCat Del-Campo-Sanchez , A. , R. Ballesteros , D. Hernandez-Lopez , J. F. Ortega , and M. A. Moreno ; Agroforestry and Cartography Precision Research Group . 2019 . Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques . PLoS One 14 : e0215521 . Google Scholar Crossref Search ADS PubMed WorldCat Delegido , J. , J. Verrelst , L. Alonso , and J. Moreno . 2011 . Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content . Sensors (Basel) . 11 : 7063 – 7081 . Google Scholar Crossref Search ADS PubMed WorldCat Delegido , J. , J. Verrelst , C. M. Meza , J. P. Rivera , L. Alonso , and J. Moreno . 2013 . A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems . Europ. J. Agronomy 46 : 42 – 52 . Google Scholar Crossref Search ADS WorldCat DJI . 2019 . AGRAS MG-1S . Available from https://www.dji.com/mg-1s Do Prado Ribeiro , L. , A. L. S. Klock , J. A. W. Filho , M. A. Tramontin , M. A. Trapp , A. Mithöfer , and C. Nansen . 2018 . Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory . Plant Methods . 14 : 54 . Google Scholar Crossref Search ADS PubMed WorldCat Drone Agriculture . 2018 . Formerly aerobugs . Available from https://www.droneagriculture.com.au/ DuPont , T . 2018 . Adding to the codling moth IPM tool box . WSU Tree Fruit . Available from http://treefruit.wsu.edu/article/adding-to-the-codling-moth-ipm-tool-box/ Ecobotix . 2018 . Available from https://www.ecobotix.com/. In Danish. Elliott , N. C. , M. Mirik , Z. Yang , T. Dvorak , M. Rao , J. Michels , T. Walker , V. Catana , M. Phoofolo , K. L. Giles , and T. Royer . 2007 . Airborne multi-spectral remote sensing of Russian wheat aphid injury to wheat . Southwest. Entomol . 32 : 213 – 219 . Google Scholar Crossref Search ADS WorldCat Elliott , N. , M. Mirik , Z. Yang , D. Jones , M. Phoofolo , V. Catana , K. Giles , and G. J. Michels . 2009 . Airborne remote sensing to detect greenbug stress to wheat . Southwest. Entomol . 34 : 205 – 211 . Google Scholar Crossref Search ADS WorldCat Elliott , N. C. , G. F. Backoulou , M. J. Brewer , and K. L. Giles . 2015 . NDVI to detect sugarcane aphid injury to grain sorghum . J. Econ. Entomol . 108 : 1452 – 1455 . Google Scholar Crossref Search ADS PubMed WorldCat Everitt , J. , D. Escobar , K. Summy , and M. Davis . 1994 . Using airborne video, global positioning system, and geographical information system technologies for detecting and mapping citrus blackfly infestations . Southwest. Entomol . 19 : 129 – 138 . OpenURL Placeholder Text WorldCat Everitt , J. , D. Escobar , K. Summy , M. Alaniz , and M. Davis . 1996 . Using spatial information technologies for detecting and mapping whitefly and harvester ant infestations in south Texas . Southwest. Entomol . 21 : 421 – 432 . OpenURL Placeholder Text WorldCat Everitt , J. H. , K. R. Summy , D. E. Escobar , and M. R. Davis . 2003 . An overview of aircraft remote sensing in integrated pest management . Subtrop. Plant Sci . 55 : 59 – 67 . OpenURL Placeholder Text WorldCat EWH BioProduction . 2019 . Available from https://bioproduction.dk/?lang=en FAA . 2016 . Press release - New FAA rules for small unmanned aircraft systems go into effect . Available from https://www.faa.gov/news/press_releases/news_story.cfm?newsId=20734 FAA . 2018a . Unmanned Aircraft Systems frequently asked questions . Available from https://www.faa.gov/uas/resources/faqs/ FAA . 2018b . Unmanned Aircraft Systems getting started . Available from https://www.faa.gov/uas/getting_started/ Faiçal , B. S. , F. G. Costa , G. Pessin , J. Ueyama , H. Freitas , A. Colombo , P. H. Fini , L. Villas , F. S. Osório , P. A. Vargas , and T. Braun . 2014a . The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides . J. Syst. Architect . 60 : 393 – 404 . Google Scholar Crossref Search ADS WorldCat Faiçal , B. S. , G. Pessin , G. P. R. Filho , A. C. P. L. F. Carvalho , G. Furquim , and J. Ueyama . 2014b . Fine-tuning of UAV control rules for spraying pesticides on crop fields, pp. 527 – 533 . In IEEE International Conference on Tools with Artificial Intelligence (ICTAI) , Limassol, Cyprus . Faiçal , B. S. , G. Pessin , G. P. R. Filho , A. C. P. L. F. Carvalho , P. H. Gomes , and J. Ueyama . 2016 . Fine-tuning of UAV control rules for spraying pesticides on crop fields: an approach for dynamic environments . Int. J. Artif. Intell. Tools 25 : 1660003 . Google Scholar Crossref Search ADS WorldCat Faiçal , B. S. , H. Freitas , P. H. Gomes , L. Y. Mano , G. Pessin , A. C. P. L. F. de Carvalho , B. Krishnamachari , and J. Ueyama . 2017 . An adaptive approach for UAV-based pesticide spraying in dynamic environments . Comput. Electron. Agric . 138 : 210 – 223 . Google Scholar Crossref Search ADS WorldCat Fan , Y. , T. Wang , Z. Qiu , J. Peng , C. Zhang , and Y. He . 2017 . Fast detection of striped stem-borer (Chilo suppressalis Walker) infested rice seedling based on visible/near-infrared hyperspectral imaging system . Sensors 17 : 2470 . Google Scholar Crossref Search ADS WorldCat Farm Journal Pulse . 2019 . Results: will you use a drone on your farm this year? Available from http://pulse.farmjournalmobile.com/index.php?campaign_id=476 Fitzgerald , G. J. , S. J. Maas , and W. R. Detar . 2004 . Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis . Precis. Agric . 5 : 275 – 289 . Google Scholar Crossref Search ADS WorldCat FlyH2 Aerospace . 2018 . Agriculture - greenfly aviation . Available from https://flyh2.com/agriculture-greenfly-aviation/ Fraulo , A. B. , M. Cohen , and O. E. Liburd . 2009 . Visible/near infrared reflectance (VNIR) spectroscopy for detecting twospotted spider mite (Acari: Tetranychidae) damage in strawberries . Environ. Entomol . 38 : 137 – 142 . Google Scholar Crossref Search ADS PubMed WorldCat Gago , J. , C. Douthe , R. Coopman , P. Gallego , M. Ribas-Carbo , J. Flexas , J. Escalona , and H. Medrano . 2015 . UAVs challenge to assess water stress for sustainable agriculture . Agric. Water Manag . 153 : 9 – 19 . Google Scholar Crossref Search ADS WorldCat Garcia-Ruiz , F. , S. Sankaran , J. M. Maja , W. S. Lee , J. Rasmussen , and R. Ehsani . 2013 . Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees . Comput. Electron. Agric . 91 : 106 – 115 . Google Scholar Crossref Search ADS WorldCat Garman , P. , and B. H. Kennedy . 1949 . Effect of soil fertilization on the rate of reproduction of the two-spotted spider mite . J. Econ. Entomol . 42 : 157 – 158 . Google Scholar Crossref Search ADS WorldCat Genc , H. , L. Genc , H. Turhan , S. Smith , and J. Nation . 2008 . Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat . Afr. J. Biotechnol . 7 : 173 – 180 . OpenURL Placeholder Text WorldCat Gerson , U. , and P. G. Weintraub . 2007 . Mites for the control of pests in protected cultivation . Pest Manag. Sci . 63 : 658 – 676 . Google Scholar Crossref Search ADS PubMed WorldCat Giles , D. K. , and R. C. Billing . 2015 . Deployment and performance of a UAV for crop spraying . Chem. Eng. Trans . 44 : 307 – 312 . OpenURL Placeholder Text WorldCat Giles , D. K. , J. Gardner , and H. Studer . 1995 . Mechanical release of predacious mites for biological pest control in strawberries . Trans. Am. Soc. Agric. Eng . 38 : 1289 – 1296 . Google Scholar Crossref Search ADS WorldCat Gillespie , A . 2015 . Dispatches - FAA gives approval to pesticide-spraying drone . Front. Ecol. Environ . 13 : 236 – 240 . Google Scholar Crossref Search ADS WorldCat Glenn , E. P. , A. R. Huete , P. L. Nagler , and S. G. Nelson . 2008 . Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape . Sensors (Basel) . 8 : 2136 – 2160 . Google Scholar Crossref Search ADS PubMed WorldCat Gonzalez , F. , A. Mcfadyen , and E. Puig . 2018 . Advances in unmanned aerial systems and payload technologies for precision agriculture, pp. 133 – 155 . In G. Chen (ed.), Advances in agricultural machinery and technologies . CRC Press , Boca Raton, FL . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Gonzalez-de-Santos , P. , A. Ribeiro , C. Fernandez-Quintanilla , F. Lopez-Granados , M. Brandstoetter , S. Tomic , S. Pedrazzi , A. Peruzzi , G. Pajares , G. Kaplanis , et al. . 2017 . Fleets of robots for environmentally-safe pest control in agriculture . Precis. Agric . 18 : 574 – 614 . Google Scholar Crossref Search ADS WorldCat Gregg , P. C. , A. P. Del Socorro , and P. J. Landolt . 2018 . Advances in attract-and-kill for agricultural pests: beyond pheromones . Annu. Rev. Entomol . 63 : 453 – 470 . Google Scholar Crossref Search ADS PubMed WorldCat Grinnan , R. , T. E. Carter , and M. T. J. Johnson . 2013 . Effects of drought, temperature, herbivory, and genotype on plant-insect interactions in soybean (Glycine max) . Arthropod Plant Interact . 7 : 201 – 215 . Google Scholar Crossref Search ADS WorldCat Gutbrodt , B. , K. Mody , and S. Dorn . 2011 . Drought changes plant chemistry and causes contrasting responses in lepidopteran herbivores . Oikos 120 : 1732 – 1740 . Google Scholar Crossref Search ADS WorldCat Gutbrodt , B. , S. Dorn , S. B. Unsicker , and K. Mody . 2012 . Species-specific responses of herbivores to within-plant and environmentally mediated between-plant variability in plant chemistry . Chemoecology 22 : 101 – 111 . Google Scholar Crossref Search ADS WorldCat Hardin , P. J. , and R. R. Jensen . 2011 . Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities . GISci. Remote Sens . 48 : 99 – 111 . Google Scholar Crossref Search ADS WorldCat Hart , W. G. , and V. I. Meyers . 1968 . Infrared aerial color photography for detection of populations of brown soft scale in citrus groves . J. Econ. Entomol . 61 : 617 – 624 . Google Scholar Crossref Search ADS WorldCat Hart , W. G. , S. J. Ingle , M. R. Davis , and C. Mangum . 1973 . Aerial photography with infrared color film as a method of surveying for citrus blackfly . J. Econ. Entomol . 66 : 190 – 194 . Google Scholar Crossref Search ADS WorldCat Herren , H. R. , T. J. Bird , and D. J. Nadel . 1987 . Technology for automated aerial release of natural enemies of the cassava mealybug and cassava green mite . Int. J. Trop. Insect Sci . 8 : 883 – 885 . Google Scholar Crossref Search ADS WorldCat Herrmann , I. , M. Berenstein , A. Sade , A. Karnieli , D. J. Bonfil , and P. G. Weintraub . 2012 . Spectral monitoring of two-spotted spider mite damage to pepper leaves . Remote Sens. Lett . 3 : 277 – 283 . Google Scholar Crossref Search ADS WorldCat Herrmann , I. , M. Berenstein , T. Paz-Kagan , A. Sade , and A. Karnieli . 2015 . Early detection of two-spotted spider mite damage to pepper leaves by spectral means, pp. 661 – 666 . In European Conference on Precision Agriculture , 12–16 July 2015 , Volcani Center, Israel . Herrmann , I. , M. Berenstein , T. Paz-Kagan , A. Sade , and A. Karnieli . 2017 . Spectral assessment of two-spotted spider mite damage levels in the leaves of greenhouse-grown pepper and bean . Biosyst. Eng . 157 : 72 – 85 . Google Scholar Crossref Search ADS WorldCat Hodgson , E. W. , E. C. Burkness , W. D. Hutchison , and D. W. Ragsdale . 2004 . Enumerative and binomial sequential sampling plans for soybean aphid (Homoptera: Aphididae) in soybean . J. Econ. Entomol . 97 : 2127 – 2136 . Google Scholar Crossref Search ADS PubMed WorldCat Hofman , V. , and E. Solseng . 2001 . Reducing spray drift. AE-1210 . North Dakota State University Extension Service , Fargo, ND . Available from https://library.ndsu.edu/ir/bitstream/handle/10365/5111/ae1210.pdf?sequence=1 Hogan , S. D. , M. Kelly , B. Stark , and Y. Chen . 2017 . Unmanned aerial systems for agriculture and natural resources . Calif. Agric . 71 : 5 – 14 . Google Scholar Crossref Search ADS WorldCat Horler , D. N. H. , M. Dockray , and J. Barber . 1983 . The red edge of plant leaf reflectance . Int. J. Remote Sens . 4 : 273 – 288 . Google Scholar Crossref Search ADS WorldCat Huang , Y. , W. C. Hoffmann , Y. Lan , W. Wu , and B. K. Fritz . 2009 . Development of a spray system for an unmanned aerial vehicle platform . Appl. Eng. Agric . 25 : 803 – 809 . Google Scholar Crossref Search ADS WorldCat Huang , W. , J. Luo , J. Zhao , J. Zhang , and Z. Ma . 2011 . Predicting wheat aphid using 2-dimensional feature space based on multi-temporal Landsat TM, pp. 1830 – 1833 . In IEEE International Geoscience and Remote Sensing Symposium , 24-29 July 2011 , Vancouver, BC, Canada . Huang , J. , H. Liao , Y. Zhu , J. Sun , Q. Sun , and X. Liu . 2012a . Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis) . Comput. Electron. Agric . 82 : 100 – 107 . Google Scholar Crossref Search ADS WorldCat Huang , W. , J. Luo , J. Zhang , J. Zhao , C. Zhao , J. Wang , G. Yang , M. Huang , L. Huang , and S. Du . 2012b . Crop disease and pest monitoring by remote sensing . In B. Escalante (ed.), Remote sensing – applications . InTech , Rijeka, Croatia . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Huang , W. , J. Luo , Q. Gong , J. Zhao , and J. Zhang . 2013 . Discriminating wheat aphid damage level using spectral correlation simulating analysis, pp. 3722 – 3725 . In IEEE International Geoscience and Remote Sensing Symposium , 21–26 July 2013 , Melbourne, VIC, Australia . Huang , W. , Q. Guan , J. Luo , J. Zhang , J. Zhao , D. Liang , L. Huang , and D. Zhang . 2014 . New optimized spectral indices for identifying and monitoring winter wheat diseases . IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens . 7 : 2516 – 2524 . Google Scholar Crossref Search ADS WorldCat Huang , J.-R. , J.-Y. Sun , H.-J. Liao , and X.-D. Liu . 2015a . Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer . Precis. Agric . 16 : 148 – 163 . Google Scholar Crossref Search ADS WorldCat Huang , J. , C. Wei , Y. Zhang , G. A. Blackburn , X. Wang , C. Wei , and J. Wang . 2015b . Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data . PLoS One 10 : e0137029 . Google Scholar Crossref Search ADS WorldCat Huang , H. , J. Deng , Y. Lan , A. Yang , X. Deng , L. Zhang , S. Wen , Y. Jiang , G. Suo , and P. Chen . 2018 . A two-stage classification approach for the detection of spider mite-infested cotton using UAV multispectral imagery . Remote Sens. Lett . 9 : 933 – 941 . Google Scholar Crossref Search ADS WorldCat Huete , A. R . 1988 . A soil-adjusted vegetation index (SAVI) . Remote Sens. Environ . 25 : 295 – 309 . Google Scholar Crossref Search ADS WorldCat Hunt , E. R. , and C. S. T. Daughtry . 2018 . What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? Int. J. Remote Sens . 39 : 5345 – 5376 . Google Scholar Crossref Search ADS WorldCat Hunt , J. E. R. , and S. I. Rondon . 2017 . Detection of potato beetle damage using remote sensing from small unmanned aircraft systems . J. Appl. Remote Sens . 11 : 026013 . Google Scholar Crossref Search ADS WorldCat Hunt , J. E. R. , S. I. Rondon , P. B. Hamm , R. W. Turner , A. E. Bruce , and J. J. Brungardt . 2016 . Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems, pp. 98660N . In SPIE Commercial + Scientific Sensing and Imaging , 17–21 April 2016 , Baltimore, MD . Iost Filho , F. H . 2019 . Remote sensing for monitoring whitefly, Bemisia tabaci biotype B (Hemiptera: Aleyrodidae) in soybean . Master’s thesis. University of São Paulo , Piracicaba, São Paulo, Brazil (in Portuguese with English abstract). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC ISCA . 2019a . Mating disruption . Available from https://www.iscatech.com/solutions/mating-disruption/ ISCA . 2019b . Attract & kill: the hybrid IPM solution . Available from https://www.iscatech.com/solutions/attract-kill/ Jorge , L. A. C. , Z. N. Brandão , and R. Y. Inamasu . 2014 . Insights and recommendations of use of UAV platforms in precision agriculture in Brazil, pp. 18 . In SPIE Remote Sensing , 22–25 September 2014 , Amsterdam, Netherlands . Jorge , J. , M. Vallbé , and J. A. Soler . 2019 . Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images . Eur. J. Remote Sens . 52 : 169 – 177 . Google Scholar Crossref Search ADS WorldCat Judd , G. J. R. , and M. G. T. Gardiner . 2005 . Towards eradication of codling moth in British Columbia by complimentary actions of mating disruption, tree banding and sterile insect technique: five-year study in organic orchards . Crop Prot . 24 : 718 – 733 . Google Scholar Crossref Search ADS WorldCat Katsoulas , N. , A. Elvanidi , K. Ferentinos , T. Bartzanas , and C. Kittas . 2016 . Calibration methodology of a hyperspectral imaging system for greenhouse plant water stress estimation . Acta Hortic . 1142 : 119 – 126 . Google Scholar Crossref Search ADS WorldCat Kim , H. G. , J.-S. Park , and D.-H. Lee . 2018 . Potential of unmanned aerial sampling for monitoring insect populations in rice fields . Fla. Entomol . 101 : 330 – 334 . Google Scholar Crossref Search ADS WorldCat Koppert . 2017a . Spidex - Phytoseiulus persimilis . Available from https://www.koppert.com/products/products-pests-diseases/spidex/ Koppert . 2017b . Mini-airbug . Available from https://www.koppert.com/products/distribution-appliances/mini-airbug/ Lacoste , C. , C. Nansen , S. Thompson , L. Moir-Barnetson , A. Mian , M. McNee , and K. C. Flower . 2015 . Increased susceptibility to aphids of flowering wheat plants exposed to low temperatures . Environ. Entomol . 44 : 610 – 618 . Google Scholar Crossref Search ADS PubMed WorldCat Lan , Y. , and S. Chen . 2018 . Current status and trends of plant protection UAV and its spraying technology in China . Int. J. Precis. Agric. Aviat . 1 : 1 – 9 . OpenURL Placeholder Text WorldCat Lan , Y. , S. J. Thomson , Y. Huang , W. C. Hoffmann , and H. Zhang . 2010 . Current status and future directions of precision aerial application for site-specific crop management in the USA . Comput. Electron. Agric . 74 : 34 – 38 . Google Scholar Crossref Search ADS WorldCat Lan , Y. , H. Zhang , J. W. Hoffmann , and J. D. Lopez . 2013 . Spectral response of spider mite infested cotton: mite density and miticide rate study . Int. J. Agric. Biol. Eng . 6 : 48 – 52 . OpenURL Placeholder Text WorldCat Larson , J. A. , R. K. Roberts , B. C. English , S. L. Larkin , M. C. Marra , S. W. Martin , K. W. Paxton , and J. M. Reeves . 2008 . Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production . Precis. Agric . 9 : 195 – 208 . Google Scholar Crossref Search ADS WorldCat Lestina , J. , M. Cook , S. Kumar , J. Morisette , P. J. Ode , and F. Peairs . 2016 . MODIS imagery improves pest risk assessment: a case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA . Environ. Entomol . 45 : 1343 – 1351 . Google Scholar Crossref Search ADS PubMed WorldCat Li , H. , W. A. Payne , G. J. Michels , and C. M. Rush . 2008 . Reducing plant abiotic and biotic stress: drought and attacks of greenbugs, corn leaf aphids and virus disease in dryland sorghum . Environ. Exp. Bot . 63 : 305 – 316 . Google Scholar Crossref Search ADS WorldCat Li , D. , X. Yuan , B. Zhang , Y. Zhao , Z. Song , and C. Zuo . 2013 . Report of using unmanned aerial vehicle to release Trichogramma . Chin. J. Biol. Control 29 : 455 – 458 (in Chinese with English abstract). OpenURL Placeholder Text WorldCat Lillesand , T. M. , R. W. Kiefer , and J. W. Chipman . 2007 . Remote sensing and image interpretation , pp. 736 . Wiley , Hoboken, NJ . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Lindblom , J. , C. Lundström , M. Ljung , and A. Jonsson . 2017 . Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies . Precis. Agric . 18 : 309 – 331 . Google Scholar Crossref Search ADS WorldCat Liu , X.-D. , and Q.-H. Sun . 2016 . Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method . Int. J. Pest Manag . 62 : 205 – 213 . Google Scholar Crossref Search ADS WorldCat Liu , Z. , J.-A. Cheng , W. Huang , C. Li , X. Xu , X. Ding , J. Shi , and B. Zhou . 2012 . Hyperspectral discrimination and response characteristics of stressed rice leaves caused by rice leaf folder, pp. 528 – 537 . In D. Li and Y. Chen (eds.), Computer and computing technologies in agriculture V. CCTA 2011. IFIP advances in information and communication technology , vol. 369 . Springer , Berlin/Heidelberg, Germany . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Liu , Z.-Y. , J.-G. Qi , N.-N. Wang , Z.-R. Zhu , J. Luo , L.-J. Liu , J. Tang , and J.-A. Cheng . 2018 . Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network . Precision Agric . 19 : 973 – 991 . Google Scholar Crossref Search ADS WorldCat Lobits , B. , L. Johnson , C. Hlavka , R. Armstrong , and C. Bell . 1997 . Grapevine remote sensing analysis of phylloxera early stress (GRAPES): remote sensing analysis summary . NASA Tech. Memo , Moffett Field, CA. 112218 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Lowe , A. , N. Harrison , and A. P. French . 2017 . Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress . Plant Methods . 13 : 80 . Google Scholar Crossref Search ADS PubMed WorldCat Luedeling , E. , A. Hale , M. Zhang , W. J. Bentley , and L. C. Dharmasri . 2009 . Remote sensing of spider mite damage in California peach orchards . Int. J. Appl. Earth Obs. Geoinf . 11 : 244 – 255 . Google Scholar Crossref Search ADS WorldCat Luo , J. , D. Wang , Y. Dong , W. Huang , and J. Wang . 2011 . Developing an aphid damage hyperspectral index for detecting aphid (Hemiptera: Aphididae) damage levels in winter wheat, pp. 1744 – 1747 . In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , 2–29 July 2011 , Vancouver, BC, Canada . Luo , J. , W. Huang , Q. Guan , J. Zhao , and J. Zhang . 2013a . Hyperspectral image for discriminating aphid and aphid damage region of winter wheat leaf, pp. 3726 – 3729 . In IEEE International Geoscience and Remote Sensing Symposium , 21–26 July 2013 , Melbourne, VIC, Australia . Luo , J. , W. Huang , L. Yuan , C. Zhao , S. Du , J. Zhang , and J. Zhao . 2013b . Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat . Precis. Agric . 14 : 151 – 161 . Google Scholar Crossref Search ADS WorldCat Luo , J. , W. Huang , J. Zhao , J. Zhang , C. Zhao , and R. Ma . 2013c . Detecting aphid density of winter wheat leaf using hyperspectral measurements . IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens . 6 : 690 – 698 . Google Scholar Crossref Search ADS WorldCat Luo , J. , W. Huang , J. Zhao , J. Zhang , R. Ma , and M. Huang . 2014 . Predicting the probability of wheat aphid occurrence using satellite remote sensing and meteorological data . Optik 125 : 5660 – 5665 . Google Scholar Crossref Search ADS WorldCat M3 Consulting Group . 2018 . Codling moth sterile insect release . Available from https://www.m3cg.us/sir/ Ma , H. , W. Huang , Y. Jing , C. Yang , L. Han , Y. Dong , H. Ye , Y. Shi , Q. Zheng , L. Liu , and C. Ruan . 2019 . Integrating growth and environmental parameters to discriminate powdery mildew and aphid of winter wheat using bi-temporal Landsat-8 imagery . Remote Sens . 11 : 846 . Google Scholar Crossref Search ADS WorldCat Machado , S. , E. D. Bynum , T. L. Archer , R. J. Lascano , L. T. Wilson , J. Bordovsky , E. Segarra , K. Bronson , D. M. Nesmith , and W. Xu . 2000 . Spatial and temporal variability of corn grain yield: site-specific relationships of biotic and abiotic factors . Precis. Agric . 2 : 359 – 376 . Google Scholar Crossref Search ADS WorldCat Maes , W. H. , and K. Steppe . 2019 . Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture . Trends Plant Sci . 24 : 152 – 164 . Google Scholar Crossref Search ADS PubMed WorldCat Mahlein , A. K. , T. Rumpf , P. Welke , H. W. Dehne , L. Plümer , U. Steiner , and E. C. Oerke . 2013 . Development of spectral indices for detecting and identifying plant diseases . Remote Sens. Environ . 128 : 21 – 30 . Google Scholar Crossref Search ADS WorldCat Martel , V. , S. Trudeau , R. Johns , E. Owens , S. M. Smith , and G. Bovin . 2018 . Testing the efficacy of Trichogramma minutum in the context of an ‘Early Intervention Strategy’ against the spruce budworm using different release methods . SERG-i Annual Reports . Quebec, Canada . pp. 276 – 283 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Martin , D. E. , and M. A. Latheef . 2017 . Remote sensing evaluation of two-spotted spider mite damage on greenhouse cotton . J. Vis. Exp . 122 : 54314 . OpenURL Placeholder Text WorldCat Martin , D. E. , and M. A. Latheef . 2018 . Active optical sensor assessment of spider mite damage on greenhouse beans and cotton . Exp. Appl. Acarol 74 : 147 – 158 . Google Scholar Crossref Search ADS PubMed WorldCat Martin , D. E. , and M. A. Latheef . 2019 . Aerial application methods control spider mites on corn in Kansas, USA . Exp. Appl. Acarol 77 : 571 – 582 . Google Scholar Crossref Search ADS PubMed WorldCat Martin , D. E. , M. A. Latheef , and J. D. López . 2015 . Evaluation of selected acaricides against twospotted spider mite (Acari: Tetranychidae) on greenhouse cotton using multispectral data . Exp. Appl. Acarol 66 : 227 – 245 . Google Scholar Crossref Search ADS PubMed WorldCat Martinez-Guanter , J. , P. Agüera , J. Agüera , and M. Pérez-Ruiz . 2019 . Spray and economics assessment of a UAV-based ultra-low-volume application in olive and citrus orchards . Precision Agric . doi: 10.1007/s11119-019-09665-7 OpenURL Placeholder Text WorldCat Crossref Matese , A. , P. Toscano , S. F. Di Gennaro , L. Genesio , F. P. Vaccari , J. Primicerio , C. Belli , A. Zaldei , R. Bianconi , and B. Gioli . 2015 . Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture . Remote Sens . 7 : 2971 – 2990 . Google Scholar Crossref Search ADS WorldCat Mattson , W. J. , and R. A. Haack . 1987 . The role of drought in outbreaks of plant-eating insects . BioScience 37 : 110 – 118 . Google Scholar Crossref Search ADS WorldCat McMurtry , J. A. , and B. A. Croft . 1997 . Life-styles of Phytoseiid mites and their roles in biological control . Annu. Rev. Entomol . 42 : 291 – 321 . Google Scholar Crossref Search ADS PubMed WorldCat Midgarden , D. , S. J. Fleischer , R. Weisz , and Z. Smilowitz . 1997 . Site-specific integrated pest management impact on development of Esfenvalerate resistance in Colorado potato beetle (Coleoptera: Chrysomelidae) and on densities of natural enemies . J. Econ. Entomol . 90 : 855 – 867 . Google Scholar Crossref Search ADS WorldCat Miller , N . 2015 . CALS researchers deploy insect ‘birth control’ to protect cranberries . University of Wisconsin-Madison News . Available from https://news.wisc.edu/cals-researchers-deploy-insect-birth-control-to-protect-cranberries/ Miller , J. R. , and L. J. Gut . 2015 . Mating disruption for the 21st century: matching technology with mechanism . Environ. Entomol . 44 : 427 – 453 . Google Scholar Crossref Search ADS PubMed WorldCat Mirik , M. , G. J. Michels , Jr , S. Kassymzhanova-Mirik , N. C. Elliott , and R. Bowling . 2006a . Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae) . J. Econ. Entomol . 99 : 1682 – 1690 . Google Scholar Crossref Search ADS WorldCat Mirik , M. , G. J. Michels , S. Kassymzhanova-Mirik , N. C Elliott , V. Catana , D. B. Jones , and R. Bowling . 2006b . Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemiptera: Aphididae) in winter wheat . Comput. Electron. Agric . 51 : 86 – 98 . Google Scholar Crossref Search ADS WorldCat Mirik , M. , G. Michels , S. Kassymzhanova-Mirik , and N. Elliott . 2007 . Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat . Comput. Electron. Agric . 57 : 123 – 134 . Google Scholar Crossref Search ADS WorldCat Mirik , M. , R. Ansley , G. Michels , and N. Elliott . 2012 . Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.) . Precis. Agric . 13 : 501 – 516 . Google Scholar Crossref Search ADS WorldCat Mirik , M. , R. J. Ansley , K. Steddom , C. M. Rush , G. J. Michels , F. Workneh , S. Cui , and N. C. Elliott . 2014 . High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier . J. Appl. Remote Sens . 8 : 083661 . Google Scholar Crossref Search ADS WorldCat Miyahara , M . 1993 . Utilization of helicopter for agriculture in Japan . Korean J. Weed Sci . 13 : 185 – 194 . OpenURL Placeholder Text WorldCat Mohite , J. , A. Gauns , N. Twarakavi , and S. Pappula . 2018 . Evaluating the capabilities of Sentinel-2 and Tetracam RGB+ 3 for multi-temporal detection of thrips on capsicum, pp. 106640U . In Autonomous air and ground sensing systems for agricultural optimization and phenotyping III , vol. 10664 . SPIE Commercial + Scientific Sensing and Imaging, 15-19 April 2018, Orlando, FL . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Mulla , D. J . 2013 . Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps . Biosyst. Eng . 114 : 358 – 371 . Google Scholar Crossref Search ADS WorldCat Myers , S. W. , and C. Gratton . 2006 . Influence of potassium fertility on soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), population dynamics at a field and regional scale . Environ. Entomol . 35 : 219 – 227 . Google Scholar Crossref Search ADS WorldCat Nansen , C . 2012 . Use of variogram parameters in analysis of hyperspectral imaging data acquired from dual-stressed crop leaves . Remote Sens . 4 : 180 – 193 . Google Scholar Crossref Search ADS WorldCat Nansen , C . 2016 . The potential and prospects of proximal remote sensing of arthropod pests . Pest Manag. Sci . 72 : 653 – 659 . Google Scholar Crossref Search ADS PubMed WorldCat Nansen , C. , and N. Elliott . 2016 . Remote sensing and reflectance profiling in entomology . Annu. Rev. Entomol . 61 : 139 – 158 . Google Scholar Crossref Search ADS PubMed WorldCat Nansen , C. , T. Macedo , R. Swanson , and D. K. Weaver . 2009 . Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants . Int. J. Remote Sens . 30 : 2447 – 2464 . Google Scholar Crossref Search ADS WorldCat Nansen , C. , A. J. Sidumo , and S. Capareda . 2010 . Variogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plants and to estimate biofuel potential . Appl. Spectrosc . 64 : 627 – 636 . Google Scholar Crossref Search ADS PubMed WorldCat Nansen , C. , A. J. Sidumo , X. Martini , K. Stefanova , and J. D. Roberts . 2013 . Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes . Comput. Electron. Agric . 97 : 21 – 26 . Google Scholar Crossref Search ADS WorldCat Nebiker , S. , N. Lack , M. Abächerli , and S. Läderach . 2016 . Light-weight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases . ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci . XLI-B1 : 963 – 970 . Google Scholar Crossref Search ADS WorldCat Nguyen , H. D. D. , and C. Nansen . 2018 . Edge-biased distributions of insects . A review. Agron Sustain. Dev . 38 : 11 . Google Scholar Crossref Search ADS WorldCat Nigam , R. , R. Kot , S. S. Sandhu , B. K. Bhattacharya , R. S. Chandi , M. Singh , J. Singh , and K. Manjunath . 2016 . Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop, pp. 98800H . In SPIE Asia-Pacific Remote Sensing Symposium , 4–7 April 2016 , New Delhi, India . Nutter , F. W. , G. L. Tylka , J. Guan , A. J. Moreira , C. C. Marett , T. R. Rosburg , J. P. Basart , and C. S. Chong . 2002 . Use of remote sensing to detect soybean cyst nematode-induced plant stress . J. Nematol . 34 : 222 – 231 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Opit , G. P. , J. R. Nechols , D. C. Margolies , and K. A. Williams . 2005 . Survival, horizontal distribution, and economics of releasing predatory mites (Acari: Phytoseiidae) using mechanical blowers . Biol. Control 33 : 344 – 351 . Google Scholar Crossref Search ADS WorldCat OPTiM . 2016 . OPTiM’s AgriDrone undergoes the world’s first successful trials for insect extermination by drone . Available from https://en.optim.co.jp/news-detail/11172 Pádua , L. , J. Vanko , J. Hruška , T. Adão , J. J. Sousa , E. Peres , and R. Morais . 2017 . UAS, sensors, and data processing in agroforestry: a review towards practical applications . Int. J. Remote Sens . 38 : 2349 – 2391 . Google Scholar Crossref Search ADS WorldCat Parabug . 2019 . Parabug , biocontrol by drone . Available from https://www.parabug.solutions/ Park , C. Y. , B.-W. Jang , J. H. Kim , C.-G. Kim , S.-M. June 2012 . Bird strike event monitoring in a composite UAV wing using high speed optical fiber sensing system . Compos. Sci. Technol . 72 : 498 – 505 . Google Scholar Crossref Search ADS WorldCat Parra , J. R. P . 2014 . Biological control in Brazil: an overview . Sci. Agric . 71 : 420 – 429 . Google Scholar Crossref Search ADS WorldCat Pearl , E . 2015 . Drone used to drop beneficial bugs on corn crop . The University of Queensland , Australia , News (UQ News). Available from https://www.uq.edu.au/news/article/2015/04/drone-used-drop-beneficial-bugs-corn-crop Pederi , Y. A. , and H. S. Cheporniuk . 2015 . Unmanned aerial vehicles and new technological methods of monitoring and crop protection in precision agriculture, pp. 298 – 301 . In IEEE International Conference Actual Problems of Unmanned Aerial Vehicles Developments , 13–15 October 2015 , Kiev, Ukraine . Peña , J. M. , J. Torres-Sánchez , A. Serrano-Pérez , A. I. de Castro , and F. López-Granados . 2015 . Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution . Sensors 15 : 5609 – 5626 . Google Scholar Crossref Search ADS PubMed WorldCat Peñuelas , J. , and I. Filella . 1998 . Visible and near-infrared reflectance techniques for diagnosing plant physiological status . Trends Plant Sci . 3 : 151 – 156 . Google Scholar Crossref Search ADS WorldCat Peñuelas , J. , I. Filella , P. Lloret , F. Munoz , and M. Vilajeliu . 1995 . Reflectance assessment of mite effects on apple trees . Int. J. Remote Sens . 16 : 2727 – 2733 . Google Scholar Crossref Search ADS WorldCat Perring , T. M. , T. O. Holtzer , J. L. Toole , and J. M. Norman . 1986 . Relationships between corn-canopy microenvironments and banks grass mite (Acari: Tetranychidae) abundance . Environ. Entomol . 15 : 79 – 83 . Google Scholar Crossref Search ADS WorldCat Pickett , C. H. , F. E. Gilstrap , R. K. Morrison , and L. F. Bouse . 1987 . Release of predatory mites (Acari: Phytoseiidae) by aircraft for the biological control of spider mites (Acari: Tetranychidae) infesting corn . J. Econ. Entomol . 80 : 906 – 910 . Google Scholar Crossref Search ADS WorldCat Pierpaoli , E. , G. Carli , E. Pignatti , and M. Canavari . 2013 . Drivers of precision agriculture technologies adoption: a literature review . Proc. Technol . 8 : 61 – 69 . Google Scholar Crossref Search ADS WorldCat Pimentel , D . 1995 . Amounts of pesticides reaching target pests: environmental impacts and ethics . J. Agric. Environ. Ethics 8 : 17 – 29 . Google Scholar Crossref Search ADS WorldCat Ponda , S. S. , L. B. Johnson , A. Geramifard , and J. P. How . 2015 . Cooperative mission planning for multi-UAV teams, pp. 1447 – 1490 . In K. P. Valavanis and G. J. Vachtsevanos (eds.), Handbook of unmanned aerial vehicles . Springer , Dordrecht, Netherlands . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Prabhakar , M. , Y. Prasad , M. Thirupathi , G. Sreedevi , B. Dharajothi , and B. Venkateswarlu . 2011 . Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae) . Comput. Electron. Agric . 79 : 189 – 198 . Google Scholar Crossref Search ADS WorldCat Prabhakar , M. , Y. G. Prasad , and M. N. Rao . 2012 . Remote sensing of biotic stress in crop plants and its applications for pest management, pp. 517 – 545 . In B. Venkateswarlu , A. K. Shanker , C. Shanker and M. Maheswari (eds.), Crop stress and its management: perspectives and strategies . Springer , Dordrecht, Netherlands . Google Scholar Crossref Search ADS Google Scholar Google Preview WorldCat COPAC Prabhakar , M. , Y. G. Prasad , S. Vennila , M. Thirupathi , G. Sreedevi , G. R. Rao , and B. Venkateswarlu . 2013 . Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton . Comput. Electron. Agric . 97 : 61 – 70 . Google Scholar Crossref Search ADS WorldCat Prasannakumar , N. , S. Chander , R. Sahoo , and V. Gupta . 2013 . Assessment of brown planthopper, (Nilaparvata lugens)[Stål], damage in rice using hyperspectral remote sensing . Int. J. Pest Manag . 59 : 180 – 188 . Google Scholar Crossref Search ADS WorldCat Prasannakumar , N. , S. Chander , and R. Sahoo . 2014 . Characterization of brown planthopper damage on rice crops through hyperspectral remote sensing under field conditions . Phytoparasitica 42 : 387 – 395 . Google Scholar Crossref Search ADS WorldCat PwC . 2016 . Clarity from above. PwC global report on the commercial applications of drone technology . Available from https://www.pwc.pl/pl/pdf/clarity-from-above-pwc.pdf Qin , W.-C. , B.-J. Qiu , X.-Y. Xue , C. Chen , Z.-F. Xu , and Q.-Q. Zhou . 2016 . Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers . Crop Prot . 85 : 79 – 88 . Google Scholar Crossref Search ADS WorldCat Quemada , M. , J. Gabriel , and P. Zarco-Tejada . 2014 . Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization . Remote Sens . 6 : 2940 – 2962 . Google Scholar Crossref Search ADS WorldCat Rangel , R. K . 2016 . Development of an UAVS distribution tools for pest’s biological control “Bug Bombs!”, pp. 1 – 8 . In IEEE Aerospace Conference , 5–12 March 2016 , Big Sky, MT . Rasmussen , J. , J. Nielsen , F. Garcia‐Ruiz , S. Christensen , J. C. Streibig , and B. Lotz . 2013 . Potential uses of small unmanned aircraft systems (UAS) in weed research . Weed Res . 53 : 242 – 248 . Google Scholar Crossref Search ADS WorldCat Raun , W. R. , J. B. Solie , G. V. Johnson , M. L. Stone , R. W. Mullen , K. W. Freeman , W. E. Thomason , and E. V. Lukina . 2002 . Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application . Agron. J . 94 : 815 – 820 . Google Scholar Crossref Search ADS WorldCat Reisig , D. , and L. Godfrey . 2006 . Remote sensing for detection of cotton aphid- (Homoptera: Aphididae) and spider mite- (Acari: Tetranychidae) infested cotton in the San Joaquin Valley . Environ. Entomol . 35 : 1635 – 1646 . Google Scholar Crossref Search ADS WorldCat Reisig , D. , and L. Godfrey . 2007 . Spectral response of cotton aphid- (Homoptera: Aphididae) and spider mite- (Acari: Tetranychidae) infested cotton: controlled studies . Environ. Entomol . 36 : 1466 – 1474 . Google Scholar Crossref Search ADS PubMed WorldCat Reisig , D. D. , and L. D. Godfrey . 2010 . Remotely sensing arthropod and nutrient stressed plants: a case study with nitrogen and cotton aphid (Hemiptera: Aphididae) . Environ. Entomol . 39 : 1255 – 1263 . Google Scholar Crossref Search ADS PubMed WorldCat Riedell , W. E. , and T. M. Blackmer . 1999 . Leaf reflectance spectra of cereal aphid-damaged wheat . Crop Sci . 39 : 1835 – 1840 . Google Scholar Crossref Search ADS WorldCat Riley , J. R . 1989 . Remote sensing in entomology . Ann. Rev. Entomol . 43 : 247 – 271 . Google Scholar Crossref Search ADS WorldCat Roberts , D. A. , K. L. Roth , and R. L. Perroy . 2001 . Hyperspectral vegetation indices, pp. 309 – 327 . In P. S. Thenkabail , J. G. Lyon , and A. Huete (eds.), Hyperspectral remote sensing of vegetation . CRC Press , Boca Raton, FL . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rodriguez , J. G . 1951 . Mineral nutrition of the two-spotted spider mite, Tetranychus bimaculatus Harvey . Ann. Entomol. Soc. Am . 44 : 511 – 526 . Google Scholar Crossref Search ADS WorldCat Rodriguez , J. G. , and R. B. Neiswander . 1949 . The effect of soil soluble salts and cultural practices on mite populations on hothouse tomatoes . J. Econ. Entomol . 42 : 56 – 59 . Google Scholar Crossref Search ADS PubMed WorldCat Rodriguez-Saona , C. , D. Polk , R. Holdcraft , D. Chinnasamy , and A. Mafra-Neto . 2010 . SPLAT-OrB reveals competitive attraction as a mechanism of mating disruption in oriental beetle (Coleoptera: Scarabaeidae) . Environ. Entomol . 39 : 1980 – 1989 . Google Scholar Crossref Search ADS PubMed WorldCat Rosenthal , G . 2017 . PPQ explores the tantalizing promise of unmanned aircraft systems . USDA APHIS . Available from https://www.aphis.usda.gov/aphis/ourfocus/planthealth/ppq-program-overview/plant-protection-today/articles/unmanned-aircraft-systems Ru , Y. , H. Zhou , Q. Fan , and X. Wu . 2011 . Design and investigation of ultra-low volume centrifugal spraying system on aerial plant protection, no. 1110663 . In ASABE Annual International Meeting , 7–10 August 2011 , Louisville, KY . Sánchez-Bayo , F. , S. Baskaran , and I. R. Kennedy . 2002 . Ecological relative risk (EcoRR): another approach for risk assessment of pesticides in agriculture . Agric. Ecosyst. Environ . 91 : 37 – 57 . Google Scholar Crossref Search ADS WorldCat Sato , A . 2003 . The RMAX helicopter UAV . Yamaha Moter Co., LTD. , Shizuoka, Japan . Available from https://pdfs.semanticscholar.org/5d80/faae7d1ffd27422df3ad6e3d08dc6bdb1920.pdf SDU . 2018 . EcoDrone . University of Southern Denmark (SDU) . Available from https://www.sdu.dk/en/om_sdu/institutter_centre/sduuascenter/researchprojects Seely , R . 2018 . Drones, joysticks, and data-driven farming, pp. 16 – 21 . In Grow . University of Wisconsin-Madison College of Agricultural and Life Sciences . Available from https://grow.cals.wisc.edu/wp-content/uploads/sites/14/2018/06/Grow-Summer2018-web.pdf Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Sétamou , M. , and D. W. Bartels . 2015 . Living on the edges: spatial niche occupation of Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae), in citrus groves . PLoS One 10 : e0131917 . Google Scholar Crossref Search ADS PubMed WorldCat Severtson , D. , K. Flower , and C. Nansen . 2015 . Nonrandom distribution of cabbage aphids (Hemiptera: Aphididae) in dryland canola (Brassicales: Brassicaceae) . Environ. Entomol . 44 : 767 – 779 . Google Scholar Crossref Search ADS PubMed WorldCat Severtson , D. , N. Callow , K. Flower , A. Neuhaus , M. Olejnik , and C. Nansen . 2016a . Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola . Precis. Agric . 17 : 659 – 677 . Google Scholar Crossref Search ADS WorldCat Severtson , D. , K. Flower , and C. Nansen . 2016b . Spatially-optimized sequential sampling plan for cabbage aphids Brevicoryne brassicae L. (Hemiptera: Aphididae) in canola fields . J. Econ. Entomol . 109 : 1929 – 1935 . Google Scholar Crossref Search ADS WorldCat Seymour , R . 2018 . Drones tested for moth drops in Okanagan orchards . Kelowna Daily Courier . Available from http://www.kelownadailycourier.ca/news/article_abc959f2-3376-11e8-8de7-efac785fe8d1.html Shah , P. A. , and J. K. Pell . 2003 . Entomopathogenic fungi as biological control agents . Appl. Microbiol. Biotechnol . 61 : 413 – 423 . Google Scholar Crossref Search ADS PubMed WorldCat Shapiro-Ilan , D. I. , R. Han , and C. Dolinksi . 2012 . Entomopathogenic nematode production and application technology . J. Nematol . 44 : 206 – 217 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Shi , Y. , W. Huang , J. Luo , L. Huang , and X. Zhou . 2017 . Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis . Comput. Electron. Agric . 141 : 171 – 180 . Google Scholar Crossref Search ADS WorldCat Shields , E. J. , and A. M. Testa . 1999 . Fall migratory flight initiation of the potato leafhopper, Empoasca fabae (Homoptera: Cicadelliade): observations in the lower atmosphere using remote piloted vehicles . Agric. For. Meteorol . 97 : 317 – 330 . Google Scholar Crossref Search ADS WorldCat Shim , D. H. , J.-S. Han , and H.-T. Yeo . 2009 . A development of unmanned helicopters for industrial applications . J. Intell. Robot. Syst . 54 : 407 – 421 . Google Scholar Crossref Search ADS WorldCat Simmons , G. S. , D. M. Suckling , J. E. Carpenter , M. F. Addison , V. A. Dyck , and M. J. B. Vreysen . 2010 . Improved quality management to enhance the efficacy of the sterile insect technique for lepidopteran pests . J. Appl. Entomol . 134 : 261 – 273 . Google Scholar Crossref Search ADS WorldCat Singh , K. , and C. Nansen . 2017 . Advanced calibration to improve robustness of drone-acquired hyperspectral remote sensing data, pp. 1 – 6 . In IEEE International Conference on Agro-Geoinformatics , 7–10 August 2017 , Fairfax, VA . Smith , S. M . 1996 . Biological control with Trichogramma: advances, successes, and potential of their use . Annu. Rev. Entomol . 41 : 375 – 406 . Google Scholar Crossref Search ADS PubMed WorldCat Souza , E. G. , P. C. Scharf , and K. A. Sudduth . 2010 . Sun position and cloud effects on reflectance and vegetation indices of corn . Agron. J . 102 : 734 – 744 . Google Scholar Crossref Search ADS WorldCat Stanton , C. , M. J. Starek , N. Elliott , M. Brewer , M. M. Maeda , and T. Chu . 2017 . Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment . J. Appl. Remote Sens . 11 : 026035 . Google Scholar Crossref Search ADS WorldCat Stark , B. , S. Rider , and Y. Chen . 2013a . Optimal pest management by networked unmanned cropdusters in precision agriculture: a cyber-physical system approach, pp. 296 – 302 . In IFAC Proceedings. IFAC Workshop on Research, Education and Development of Unmanned Aerial Systems , 20–22 November 2013 , Compiegne, France . Stark , B. , B. Smith , and Y. Chen . 2013b . A guide for selecting small unmanned aerial systems for research-centric applications, pp. 38 – 45 . In IFAC Proceedings. IFAC Workshop on Research, Education and Development of Unmanned Aerial Systems , 20–22 November 2013 , Compiegne, France . Steffan , S. A. , E. M. Chasen , A. E. Deutsch , and A. Mafram-Neto . 2017 . Multi-species mating disruption in cranberries (Ericales: Ericaceae): early evidence using a flowable emulsion . J. Insect Sci . 17 : 54 . Google Scholar Crossref Search ADS WorldCat Stiefel , V. L. , D. C. Margolies , and P. J. Bramel-Cox . 1992 . Leaf temperature affects resistance to the banks grass mite (Acari: Tetranychidae) on drought-resistant grain sorghum . J. Econ. Entomol . 85 : 2170 – 2184 . Google Scholar Crossref Search ADS WorldCat Stöcker , C. , R. Bennett , F. Nex , M. Gerke , and J. Zevenbergen . 2017 . Review of the current state of UAV regulations . Remote Sens . 9 : 459 . Google Scholar Crossref Search ADS WorldCat Stone , C. , and C. Mohammed . 2017 . Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: a review . Curr. For. Rep . 3 : 75 – 92 . OpenURL Placeholder Text WorldCat Stumph , B. , M. Hernandez Virto , H. Medeiros , A. Tabb , S. Wolford , K. Rice , and T. Leskey . 2019 . Detecting invasive insects with unmanned aerial vehicles . In IEEE International Conference on Robotics and Automation (ICRA) , 20–24 May 2019 , Montreal, Canada . Sudbrink , D. , F. Harris , J. Robbins , P. English , and J. Willers . 2003 . Evaluation of remote sensing to identify variability in cotton plant growth and correlation with larval densities of beet armyworm and cabbage looper (Lepidoptera: Noctuidae) . Fla. Entomol . 86 : 290 – 294 . Google Scholar Crossref Search ADS WorldCat Sudbrink , D. L. , S. J. Thomson , R. S. Fletcher , F. A. Harris , P. J. English , and J. T. Robbins . 2015 . Remote sensing of selected winter and spring host plants of tarnished plant bug (Heteroptera: Miridae) and herbicide use strategies as a management tactic . Am. J. Plant Sci . 6 : 1313 – 1327 . Google Scholar Crossref Search ADS WorldCat Sylvester , G . 2018 . E-agriculture in action: drones for agriculture . Food and Agriculture Organization of the United Nations and International Telecommunication Union , Bangkok, Thailand . Available from http://www.fao.org/3/i8494en/i8494en.pdf Tahir , N. , and G. Brooker . 2009 . Feasibility of UAV based optical tracker for tracking Australian plague locust, pp. 1 – 10 . In Australasian Conference on Robotics and Automation , 2–4 December 2009 , Sydney, NSW, Australia . Tan , L. T. , and K. H. Tan . 2013 . Alternative air vehicles for sterile insect technique aerial release . J. Appl. Entomol . 137 : 126 – 141 . Google Scholar Crossref Search ADS WorldCat Tan , Y. , J.-Y. Sun , B. Zhang , M. Chen , Y. Liu , and X.-D. Liu . 2019 . Sensitivity of a ratio vegetation index derived from hyperspectral remote sensing to the brown planthopper stress on rice plants . Sensors 19 : 375 . Google Scholar Crossref Search ADS WorldCat Tang , Z. , Y. Li , J. Zhao , and D. Hu . 2016 . Research on trajectory planning algorithm of plant-protective UAV, pp. 110 – 113 . In IEEE International Conference on Aircraft Utility Systems , 10–12 October 2016 , Beijing, China . Teal Group . 2019 . Teal Group predicts worldwide civil drone production will almost triple over the next decade . Available from https://www.tealgroup.com/index.php/pages/press-releases/60-teal-group-predicts-worldwide-civil-drone-production-will-almost-triple-over-the-next-decade Teske , M. E. , S. L. Bird , D. M. Esterly , T. B. Curbishley , S. L. Ray , and S. G. Perry . 2002 . AgDRIFT: a model for estimating near-field spray drift from aerial applications . Environ. Toxicol. Chem . 21 : 659 – 671 . Google Scholar Crossref Search ADS PubMed WorldCat Teske A. L. , G. Chen , C. Nansen , and Z. Kong . 2019 . Optimised dispensing of predatory mites by multirotor UAVs in wind: a distribution pattern modelling approach for precision pest management . Biosyst. Eng . 187 : 226 – 238 . Google Scholar Crossref Search ADS
TI - Drones: Innovative Technology for Use in Precision Pest Management
JF - Journal of Economic Entomology
DO - 10.1093/jee/toz268
DA - 2020-02-08
UR - https://www.deepdyve.com/lp/oxford-university-press/drones-innovative-technology-for-use-in-precision-pest-management-SBdf7xmrbo
SP - 1
VL - 113
IS - 1
DP - DeepDyve
ER -