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Agriculture
Special Issues
Design and Development of Smart Crop Protection Equipment
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Design and Development of Smart Crop Protection Equipment

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (25 August 2025) | Viewed by 11395

Special Issue Editors

Prof. Dr. Weidong Jia

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: two-phase flow theory and application; precision crop protection spraying technology; intelligent crop protection equipment
Prof. Dr. Qizhi Yang

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: agricultural robot; parallel mechanism and its application in agricultural engineering; cash crop field management machinery; transplanting theory of plug seedlings and its automation equipment
Dr. Xiaowen Wang

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: efficient utilization of agricultural water and soil resources; intelligent water and fertilizer integration equipment; agricultural remote sensing; agricultural numerical model

Special Issue Information

Dear Colleagues,

The application of crop protection equipment to spray chemical pesticides can significantly improve agricultural production efficiency, reduce manual labor intensity, and minimize the harm of pesticides to people. This has become the most important method for crop pest and disease prevention and control at present. However, traditional crop protection equipment still faces problems such as low effective utilization of pesticides and severe pesticide droplet drift pollution. Based on the rapid development of information technology, plant phenotype, and advanced manufacturing, smart crop protection equipment is formed by deeply integrating emerging technologies such as big data, remote sensing, and artificial intelligence with the development of agricultural equipment. This equipment can realize independent accurate and variable spray operation, innovate the prevention and management of crop diseases and pests, and become a major support to promote the transformation and upgrading of modern agriculture.

This Special Issue aims to introduce innovative theories, methods and applications of smart crop protection technology and equipment. Topics of interest include, but are not limited to, the following: efficient and precise pesticide spraying technology and equipment; droplet deposition and drift control; construction and simulation of spray numerical model; remote sensing detection of crops, pests, diseases, and weeds; pesticide spraying decision; key components for precise spraying; crop protection robot; crop protection UAV and low-altitude and low-volume aerial pesticide application; low carbon drive and multi-machine collaboration for crop protection equipment; process monitoring and effect evaluation of crop protection equipment; intelligent mechanical weeding equipment. Various types of articles are welcome for submission, including original research, reviews, communications, etc.

Prof. Dr. Weidong Jia
Prof. Dr. Qizhi Yang
Dr. Xiaowen Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart crop protection equipment
  • pesticide droplet drift
  • crop protection robot
  • aviation crop protection
  • remote sensing
  • agricultural sensor

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Published Papers (7 papers)

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Research

13 pages, 1554 KB  
Article
Quantification and Optimization of Straight-Line Attitude Control for Orchard Weeding Robots Using Adaptive Pure Pursuit
by Weidong Jia, Zhenlei Zhang, Xiang Dong, Mingxiong Ou, Ronghua Gao, Yunfei Wang, Qizhi Yang and Xiaowen Wang
Agriculture 2025, 15(19), 2085; https://doi.org/10.3390/agriculture15192085 - 7 Oct 2025
Viewed by 512
Abstract
In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes [...] Read more.
In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°2), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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23 pages, 12546 KB  
Article
Performance Evaluation of a UAV-Based Graded Precision Spraying System: Analysis of Spray Accuracy, Response Errors, and Field Efficacy
by Yang Lyu, Seung-Hwa Yu, Chun-Gu Lee, Pingan Wang, Yeong-Ho Kang, Dae-Hyun Lee and Xiongzhe Han
Agriculture 2025, 15(19), 2070; https://doi.org/10.3390/agriculture15192070 - 2 Oct 2025
Viewed by 1167
Abstract
Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an [...] Read more.
Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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24 pages, 73556 KB  
Article
Neural Network-Guided Smart Trap for Selective Monitoring of Nocturnal Pest Insects in Agriculture
by Joel Hinojosa-Dávalos, Miguel Ángel Robles-García, Melesio Gutiérrez-Lomelí, Ariadna Berenice Flores Jiménez and Cuauhtémoc Acosta Lúa
Agriculture 2025, 15(14), 1562; https://doi.org/10.3390/agriculture15141562 - 21 Jul 2025
Cited by 2 | Viewed by 2109
Abstract
Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and [...] Read more.
Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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21 pages, 10154 KB  
Article
Development of EV Crawler-Type Weeding Robot for Organic Onion
by Liangliang Yang, Sota Kamata, Yohei Hoshino, Yufei Liu and Chiaki Tomioka
Agriculture 2025, 15(1), 2; https://doi.org/10.3390/agriculture15010002 - 24 Dec 2024
Cited by 2 | Viewed by 1986
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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21 pages, 3485 KB  
Article
Development and Experiment of an Air-Assisted Sprayer for Vineyard Pesticide Application
by Mingxiong Ou, Yong Zhang, Minmin Wu, Chenyang Wang, Shiqun Dai, Ming Wang, Xiang Dong and Li Jiang
Agriculture 2024, 14(12), 2279; https://doi.org/10.3390/agriculture14122279 - 12 Dec 2024
Cited by 10 | Viewed by 1623
Abstract
This paper presents an air-assisted sprayer for vineyard pesticide application. The spraying unit was designed with two symmetrically arranged ports. The airflow velocity distribution of the sprayer was investigated using a combination of experimental validation and a computational fluid dynamics (CFD) model. The [...] Read more.
This paper presents an air-assisted sprayer for vineyard pesticide application. The spraying unit was designed with two symmetrically arranged ports. The airflow velocity distribution of the sprayer was investigated using a combination of experimental validation and a computational fluid dynamics (CFD) model. The results of both the simulation and the experiment showed good agreement in airflow velocity, and the distribution was uniform. Both unilateral and bilateral spraying field experiments were conducted in this study. The unilateral spraying experiment showed that higher spray pressure and lower sprayer speed increased both total deposition coverage and spray penetration (SP), while shorter spray distances improved SP but decreased total deposition coverage. The optimal operational conditions for the sprayer were determined as follows: spray pressure of 0.40 MPa, sprayer speed of 0.83 m/s, and spray distance of 1.00 m. The results of the bilateral spraying field experiment indicated that the coefficient of variation (CV) for deposition coverage in Columns A, B, and C were 16.20%, 8.10%, and 15.47%, respectively. The CVs in Layers a, b, and c were 6.14%, 12.62%, and 6.74%, respectively. This result demonstrated that the deposition coverage distribution in the canopy was relatively uniform, and the air-assisted sprayer exhibited good spray penetration performance. This study demonstrates the effectiveness and potential of the air-assisted sprayer for vineyard pesticide application. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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16 pages, 3559 KB  
Article
Development and Evaluation of a Monodisperse Droplet-Generation System for Precision Herbicide Application
by Minmin Wu, Mingxiong Ou, Yong Zhang, Weidong Jia, Shiqun Dai, Ming Wang, Xiang Dong, Xiaowen Wang and Li Jiang
Agriculture 2024, 14(11), 1885; https://doi.org/10.3390/agriculture14111885 - 24 Oct 2024
Cited by 4 | Viewed by 1321
Abstract
Traditional methods of weed control during field management often result in herbicide waste. Precision herbicide application is crucial in agricultural production. This study presents a monodisperse droplet-generation system designed for precision herbicide application, capable of generating monodisperse droplets induced by an electric field. [...] Read more.
Traditional methods of weed control during field management often result in herbicide waste. Precision herbicide application is crucial in agricultural production. This study presents a monodisperse droplet-generation system designed for precision herbicide application, capable of generating monodisperse droplets induced by an electric field. Droplet-generation experiments were conducted to investigate the effects of capillary tube outlet shape, liquid flow rate, and capillary tube size on the generation of charged droplets. A droplet diameter prediction model was established based on the system parameters. Experimental results indicated that as the applied voltage increased, the droplet diameter decreased, and the droplet-generation patterns transitioned sequentially from dripping, micro-dripping, to unstable dripping modes. In a weak electric field, capillaries with beveled outlets produced smaller droplets with more stable diameter distributions compared to those with blunt outlets. In a strong electric field, the smallest droplet diameter from blunt capillaries was 138.2 μm, whereas from beveled capillaries it was 198.7 μm. Within the design parameter range, droplet diameter was basically positively correlated with liquid flow rate and capillary tube size. By controlling the applied voltage, liquid flow rate, and capillary tube size, stable droplet generation could be achieved within a diameter range of 198.7–2520.8 μm, and the coefficient of variation of droplet diameter under the same working conditions was generally less than 6%. The monodisperse droplet-generation system developed in this study can effectively reduce herbicide usage and improve application efficiency. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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26 pages, 7260 KB  
Article
Optimization of a Boom Height Ultrasonic Detecting Model for the Whole Growth Cycle of Wheat: Affected by the Oscillation of the Three-Section Boom of the Sprayer
by Jianguo Wu, Shuo Yang, Yuanyuan Gao, Xiaoyong Pan, Wei Zou, Yibo Wei, Changyuan Zhai and Liping Chen
Agriculture 2024, 14(10), 1733; https://doi.org/10.3390/agriculture14101733 - 1 Oct 2024
Cited by 9 | Viewed by 1408
Abstract
In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire [...] Read more.
In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height. Full article
(This article belongs to the Special Issue Design and Development of Smart Crop Protection Equipment)
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