Agriculture

Hadj Abdelkader, Oussama;Bouzebiba, Hadjer;Santos, Miguel G.;Pena, Danilo;Aguiar, António Pedro;Carvalho, Susana M. P.

doi: 10.3390/agriculture15010001pmid: N/A

This paper proposes a mathematical model based on a mechanistic approach and previous research findings for the bacterial canker disease development in kiwifruit vines. This disease is a leading cause of severe damage to kiwifruit vines, particularly in humid regions, and contributes to significant economic challenges for growers in many countries. The proposed model contains three parts. The first one is the model of the kiwifruit vine describing its light interception, its carbon acquisition, and the partitioning dynamics. The carbon resource represents the chemical energy required for maintaining the necessary respiration of the living organs and their growth processes. The second part of the model is the dynamics of the pathogenic bacterial population living within the vine’s tissues and competing with them for the carbon resource required for their proliferation. The third part of the model is the carbon dynamics described by a mass conservation formula which computes the remaining amount of carbon available for competition. The model was validated by comparing simulations with experimental results obtained from growth chambers. The results show that the proposed model can simulate reasonably well the functional part of the vine in both the healthy case and the disease case without plant defense mechanisms in which the bacteria are always dominant under favorable environmental conditions. They also show that the environmental effects on the vine’s growth and the infection progress are taken into account and align with the previous studies. The model can be used to simulate the infection process, predict its outcomes, test disease management techniques, and support experimental analyses.

Yang, Liangliang;Kamata, Sota;Hoshino, Yohei;Liu, Yufei;Tomioka, Chiaki

doi: 10.3390/agriculture15010002pmid: N/A

The decline in the number of essential farmers has become a significant issue in Japanese agriculture. In response, there is increasing interest in the electrification and automation of agricultural machinery, particularly in relation to the United Nations Sustainable Development Goals (SDGs). This study focuses on the development of an electric vehicle (EV) crawler-type robot designed for weed cultivation operations, with the aim of reducing herbicide use in organic onion farming. Weed cultivation requires precise, delicate operations over extended periods, making it a physically and mentally demanding task. To alleviate the labor burden associated with weeding, we employed a color camera to capture crop images and used artificial intelligence (AI) to identify crop rows. An automated system was developed in which the EV crawler followed the identified crop rows. The recognition data were transmitted to a control PC, which directed the crawler’s movements via motor drivers equipped with Controller Area Network (CAN) communication. Based on the crop row recognition results, the system adjusted motor speed differentials, enabling the EV crawler to follow the crop rows with a high precision. Field experiments demonstrated the effectiveness of the system, with automated operations maintaining a lateral deviation of ±2.3 cm, compared to a maximum error of ±10 cm in manual operation. These results indicate that the automation system provides a greater accuracy and is suitable for weed cultivation tasks in organic farming.

Cardil, Alba;Gallizo, José Luis;Salvador, Manuel

doi: 10.3390/agriculture15010003pmid: N/A

This work aimed to identify the locational, territorial and financial characteristics that impact the profitability of companies in the Spanish pig sector. The data were extracted from the SABI database, which contains economic and financial information. A sample of 1247 Spanish companies (14,254 observations) was obtained, providing an unbalanced panel dataset for the 2004–2018 period. The statistical analysis considered factors that potentially influence the profitability of companies, considering the potential existence of endogeneity issues among some of the variables analyzed. Companies tended to be located in autonomous communities in inland areas, which had higher depopulation rates and shorter average distances from companies to feed mills and slaughterhouses. There was regional specialization, which was influenced by the ability to invest in infrastructure, proximity to the markets, farm size and efficiency in resource management, which had a positive influence on profitability. These factors led to differences between regions, together with the support of public administration for companies that invest in sparsely populated areas. The results obtained will be of interest to policymakers developing measures aimed at providing better access to inputs through proximity to feed mills and slaughterhouses, as well as to new entrepreneurs in the sector who want to establish their businesses in the most specialized regions.

Chen, Linfeng;Zhang, Lei;Li, Le;Zhang, Lihua

doi: 10.3390/agriculture15010004pmid: N/A

Aiming to improve the existing corn cylindrical drum threshing process, where the high-frequency impact of threshing elements and rubbing actions result in a high kernel breakage rate, a radial telescopic drum taper adjustment method was proposed, and a corn conical threshing drum was developed. The threshing process was analyzed by combining theory and simulation, and the conical drum enabled rapid threshing and timely penetration of the grains through the sieve, reduced the damage caused by high-frequency action on the grains, and improved the uniformity of the distribution of the discharged material. The response surface test was carried out with the drum speed, feeding quantity, and large end diameter of the drum as the influencing factors and the damage rate as the evaluation index. The results showed that when the rotating speed of the drum was 340 r/min, the feeding quantity was 2.277 kg/s, the diameter of the big end was 506.7 mm, and the breakage rate was 2.826%. The threshing effect was found to be better than when using a cylindrical drum. The research results can provide a reference for low-damage and high-efficiency harvesting of grain.

Chen, Yinge;Wang, Lulu;Zhang, Yawei;Zheng, Nan;Zhang, Yuanqing;Zhang, Yangdong

doi: 10.3390/agriculture15010005pmid: N/A

To investigate the source of the bitter almond taste in whole corn silage (WPCS), headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC-MS), headspace gas chromatography–ion migration spectrometry (HS-GC-IMS), and electronic nose (E-nose) technology were employed. The study analyzed the differences in volatile compounds between two WPCS samples with distinct odors from the same cellar. GC-IMS and GC-MS identified 32 and 101 volatile organic compounds (VOCs), respectively, including aldehydes, alcohols, esters, ketones, and other compounds. Three characteristic volatile organic compounds associated with the bitter almond taste were detected: benzaldehyde, cyanide, and isocyanate. The electronic nose demonstrated varying sensitivities across its sensors, and principal component analysis (PCA) combined with variable importance projection (VIP) analysis revealed that W5S (nitrogen oxides) could differentiate between the two distinct silage odors. This finding was consistent with the GC-MS results, which identified 34 nitrogen-containing heterocyclic compounds in the abnormal silage sample, accounting for 77% of the total nitrogen-containing compounds. In summary, significant differences in aroma composition were observed between the bitter almond-flavored silage and the other silage in the same cellar. These differences were primarily attributed to changes in volatile organic compounds, which could serve as indicators for identifying bitter almond-flavored silage.

Ma, Hanyu;Wen, Weiliang;Gou, Wenbo;Liang, Yuqiang;Zhang, Minggang;Fan, Jiangchuan;Gu, Shenghao;Zhang, Dongsheng;Guo, Xinyu

doi: 10.3390/agriculture15010006pmid: N/A

The spatial and temporal dynamics of crop canopy structure are influenced by cultivar, environment, and crop management practices. However, continuous and automatic monitoring of crop canopy structure is still challenging. A three-dimensional (3D) time-series phenotyping study of maize canopy was conducted using a rail-driven high-throughput plant phenotyping platform (HTPPP) in field conditions. An adaptive sliding window segmentation algorithm was proposed to obtain plots and rows from canopy point clouds. Maximum height (Hmax), mean height (Hmean), and canopy cover (CC) of each plot were extracted, and quantification of plot canopy height uniformity (CHU) and marginal effect (MEH) was achieved. The results showed that the average mIoU, mP, mR, and mF1 of canopy–plot segmentation were 0.8118, 0.9587, 0.9969, and 0.9771, respectively, and the average mIoU, mP, mR, and mF1 of plot–row segmentation were 0.7566, 0.8764, 0.9292, and 0.8974, respectively. The average RMSE of plant height across the 10 growth stages was 0.08 m. The extracted time-series phenotypes show that CHU tended to vary from uniformity to nonuniformity and continued to fluctuate during the whole growth stages, and the MEH of the canopy tended to increase negatively over time. This study provides automated and practical means for 3D time-series phenotype monitoring of plant canopies with the HTPPP.

Xu, Nan;Xin, Zhenbo;Yuan, Jin;Gao, Zenghui;Tian, Yu;Xia, Chao;Liu, Xuemei;Wang, Dongwei

doi: 10.3390/agriculture15010007pmid: N/A

There are approximately 36.7 million hectares of saline alkali land available in China. To enhance the comprehensive utilization value of coastal saline alkali land and boost crop yields in such areas, it is essential to conduct research on optimizing the operational performance of high-performance soil contact components in light of the soil characteristics of coastal saline alkali land. Discrete element simulation can be employed to investigate the operational mechanisms of various key components. Nevertheless, at present, there is a dearth of discrete element models for the key physical parameters and soil structure of coastal saline alkali land soil. In this article, typical coastal saline alkali field soil was sampled, and the physical properties of the saline alkali soil, including salt content, moisture content, particle size distribution, and particle size, as well as intrinsic parameters such as soil compaction, density, Poisson’s ratio, and shear modulus, were measured. The Hertz Mindlin with Bonding contact model was employed. Physical experiments on soil accumulation angles at different depths were carried out using the cylindrical lifting method. Subsequently, by means of the discrete element method and the BBD experimental design method, a response surface model was established, and an optimization analysis was performed on the optimal parameters for the soil–soil collision recovery coefficient, static friction coefficient, and dynamic friction coefficient at each depth. Test benches for measuring the collision recovery coefficient, static friction coefficient, and rolling friction coefficient of saline alkali soil at -65Mn were set up, calculation formulas for each parameter were derived, and the contact parameters between soil at different depths and 65Mn were obtained. The results of the sliding friction angle test on different depths of saline alkali soil at -65Mn were further verified using the discrete element method, with a maximum error of 3.11%, which falls within the allowable range. This suggests that the calibration results of the discrete element simulation parameters for the interaction between soil and contact components are reliable, providing data and model support for future research on enhancing the operational performance of high-performance contact components.

Liu, Wei;Zhou, Jinhao;Zhang, Tengfei;Zhang, Pengcheng;Yao, Mengjiao;Li, Jinhong;Sun, Zitong;Ma, Guoxin;Chen, Xinxin;Hu, Jianping

doi: 10.3390/agriculture15010008pmid: N/A

The operational performance of cereal seeding machinery influences the yield and quality of cereals. In this article, we review the existing literature on intelligent technologies for cereal seeding machinery, encompassing active controllable seeding actuators, intelligent seeding rate control, and intelligent seed position control systems. In this manuscript, (1) the characteristics and innovative structures of existing motor-driven seed-metering devices and ground surface profiling mechanisms are expounded; (2) state-of-the-art detection principles and applications for soil property sensors are described based on different soil properties; (3) optimal seeding rate decision approaches based on soil properties are summarized; (4) the research state of seeding rate measuring and control technologies is expounded in detail; (5) trajectory control methods for seeding machinery and seeding depth control systems are described based on measurement and control principles; and (6) the present state, limitations, and future development directions of intelligent cereal seeding machinery are described. In the future, more advanced multi-algorithm and multi-sensor fusion technologies for soil property detection, optimal seeding rate decisions, seeding rates, and seed position control are likely to evolve. This review not only expounds the latest studies on intelligent actuating, sensing, and control technologies for intelligent cereal seeding machinery, but also discusses the shortcomings of existing intelligent seeding technologies and future developing trends in detail. This review, therefore, offers a reference for future research in the domain of intelligent seeding machinery for cereals.

Borgo, Lucélia;Rabêlo, Flávio Henrique Silveira;Marchiori, Paulo Eduardo Ribeiro;Guilherme, Luiz Roberto Guimarães;Guerra-Guimarães, Leonor;Resende, Mário Lúcio Vilela de

doi: 10.3390/agriculture15010009pmid: N/A

Abiotic stresses significantly disrupt plant physiology at the molecular, biochemical, and morphological levels, often causing irreversible damage. To ensure sustainable coffee production, it is essential to understand how environmental stresses—such as drought, heat, excess light, and salinity—affect plant growth, and to develop strategies to mitigate their impact. Despite the limited number of studies on this topic, compiling existing knowledge can provide valuable insights into how coffee plants respond to such stresses. Specifically, understanding whether coffee plants can endure damage caused by these stresses and the mechanisms they employ to do so is critical. This review aims to (i) summarize key findings on the effects of drought, heat, excess light, and salinity on coffee plants and their coping mechanisms; and (ii) explore plant breeding and nutrition as potential strategies to mitigate these abiotic stresses and enhance coffee production.

Amenyedzi, Destiny Kwabla;Kazeneza, Micheline;Mwaisekwa, Ipyana Issah;Nzanywayingoma, Frederic;Nsengiyumva, Philibert;Bamurigire, Peace;Ndashimye, Emmanuel;Vodacek, Anthony

doi: 10.3390/agriculture15010010pmid: N/A

Crop damage attributed to pest birds is an important problem, particularly in low-income countries. This paper describes a prototype system for pest bird detection using a Conv1D neural network model followed by scaring actions to reduce the presence of pest birds on farms. Acoustic recorders were deployed on farms for data collection, supplemented by acoustic libraries. The sounds of pest bird species were identified and labeled. The labeled data were used in Edge Impulse to train a tinyML Conv1D model to detect birds of interest. The model was deployed on Arduino Nano 33 BLE Sense (nodes) and XIAO (Base station) microcontrollers to detect the pest birds, and based on the detection, scaring sounds were played to deter the birds. The model achieved an accuracy of 96.1% during training and 92.99% during testing. The testing F1 score was 0.94, and the ROC score was 0.99, signifying a good discriminatory ability of the model. The prototype was able to make inferences in 53 ms using only 14.8 k of peak RAM and only 43.8 K of flash memory to store the model. Results from the prototype deployment in the field demonstrated successful detection and triggering actions and SMS messaging notifications. Further development of this novel integrated and sustainable solution will add another tool for dealing with pest birds.