Increasing agricultural efficiency in a sustainable manner will contribute to feed a growing population under limited land, nutrient and water resources. Water scarcity and the increasing social concern for this resource are already requiring more sophisticated irrigation and decision-support systems. To address the heterogeneity in crop water status in a commercial field, precision irrigation requires accurate information about crops (e.g., crop water status), soil (e.g., moisture content) and weather (e.g., wind speed and vapor pressure deficit). Numerous studies have shown that plant canopy temperature can be used to derive reliable plant water stress indicators, thus making it a promising tool for irrigation water management. However, efficient and cost-effective measurement techniques are still lacking. This paper assesses the potential of infrared thermometry and thermal imaging for monitoring plant water stress in a commercial sugar beet field by comparing canopy temperature data acquired from a conventional thermal camera with an inexpensive infrared sensor, both mounted on a rotary-wing unmanned aerial vehicle (UAV). Measurements were taken at various phenological stages of the sugar beet growing season. Laboratory tests were performed to determine the key features for accurate temperature measurements and flight altitude. Experiments were conducted in 2014 and 2015 in experimental and commercial sugar beet fields in Southwestern Spain to (i) develop an affordable infrared temperature system suitable for mounting on a UAV to obtain thermal information, (ii) compare sugar beet canopy temperature measurements collected with the low-cost platform with those obtained from a conventional thermal camera, both mounted on a rotary-wing UAV, (iii) identify the factors that will limit the use of the low-cost system to derive temperature-based water stress indices. To accomplish these objectives, well-watered and deficit irrigated plots were established. Results indicated that the lightweight canopy temperature system was robust and reliable, although there were some constraints related to weather conditions and delimitation of the area covered by the infrared sensor.
Precision Agriculture – Springer Journals
Published: Aug 20, 2016
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