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Agronomy
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Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition
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Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 8670

Special Issue Editors

Prof. Dr. Xiongkui He

E-Mail Website
Guest Editor
College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China
Interests: spray deposition and drift; plant protection equipment; high efficiency pesticide application equipment; precisely variable application technology; smart agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Baijing Qiu

E-Mail Website
Guest Editor
1. Key Laboratory of Plant Protection Engineering, Ministry of Agriculture and Rural Affairs, Jiangsu University, Zhenjiang 212013, China
2. Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
Interests: ground and aviation plant protection machinery; modern design and test technology of agricultural machinery; agricultural environment and plant protection equipment and technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fuzeng Yang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: unmanned machinery in hilly and mountainous areas; key technologies of intelligent agricultural machinery; key technologies of multi-body robot; research on information or control technology based on unmanned machinery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural environment refers to the combination of various natural and artificially modified natural factors that affect the survival and development of agricultural organisms, including farmland, forest, grassland, irrigation water, air, light, heat, and chemical fertilizer applied to farmland, pesticides, and agricultural equipment. These factors constitute a comprehensive agricultural environment system, interacting with each other and affecting agricultural production together.

As a vital component of agricultural environment, plant protection equipment plays an indispensable role in agricultural production. The intellectualization of plant protection equipment is an important link to drive agricultural development processes. With the advantages of saving time and effort, precision and high efficiency, intelligent plant protection equipment makes great contributions to cost reduction, increasing incomes, as well as to the healthy and sustainable development of agriculture.

Previously, we successfully published a Special Issue titled “Agricultural Environment and Intelligent Plant Protection Equipment”. We now, therefore, propose a second volume of this Special Issue for a broader range of applications. We aim to exchange knowledge on any aspect related to the agricultural environment and intelligent plant protection equipment to promote sustainable agricultural development.

Prof. Dr. Xiongkui He
Prof. Dr. Baijing Qiu
Prof. Dr. Fuzeng Yang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Agronomy is an international peer-reviewed open access monthly 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

  • agricultural environment
  • pollution
  • green plant protection
  • intelligent plant protection equipment
  • agricultural robot

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Related Special Issue

Published Papers (8 papers)

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Research

23 pages, 13679 KiB  
Article
Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards
by Li Zhang, Zhihui He, Haobin Zhu, Zhanhong Wei, Juan Lu and Xiongkui He
Agronomy 2025, 15(5), 1230; https://doi.org/10.3390/agronomy15051230 - 18 May 2025
Viewed by 384
Abstract
To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing [...] Read more.
To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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27 pages, 4552 KiB  
Article
Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring
by Yushuai Wang, Yuxin Chen, Zhou Shu, Shaolong Zhu, Weijun Zhang, Tao Liu and Chengming Sun
Agronomy 2025, 15(5), 1189; https://doi.org/10.3390/agronomy15051189 - 14 May 2025
Viewed by 699
Abstract
Taro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient [...] Read more.
Taro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient monitoring of taro blight. Field survey data were integrated with these images to develop a model for monitoring taro blight severity. The back propagation neural network (BPNN) model showed optimal performance during the early and middle stages of taro formation when hyperspectral parameters were used as input variables. In the early stage, the BPNN model achieved a coefficient of determination (R2) of 0.92 and an RMSE of 0.054 on the training set, and it obtained an R2 of 0.89 with a root mean square error (RMSE) of 0.074 on the validation set. The random forest regression (RFR) model performed best during the early stage of taro formation with multispectral vegetation indices as input variables. The models exhibited robust predictive capabilities across various stages, especially during the early stage of taro formation. The results demonstrate that UAV remote sensing, combined with characteristic parameters and disease indices, presents a precise taro blight monitoring method that can substantially improve disease management in taro cultivation. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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19 pages, 8993 KiB  
Article
Segmentation-Based Detection for Luffa Seedling Grading Using the Seg-FL Model
by Sheng Jiang, Fangnan Xie, Jiangbo Ao, Yechen Wei, Jingye Lu, Shilei Lyu and Zhen Li
Agronomy 2024, 14(11), 2557; https://doi.org/10.3390/agronomy14112557 - 31 Oct 2024
Viewed by 779
Abstract
This study addresses the issue of inaccurate and error-prone grading judgments in luffa plug seedlings. A new Seg-FL seedling segmentation model is proposed as an extension of the YOLOv5s-Seg model. The small leaves of early-stage luffa seedlings are liable to be mistaken for [...] Read more.
This study addresses the issue of inaccurate and error-prone grading judgments in luffa plug seedlings. A new Seg-FL seedling segmentation model is proposed as an extension of the YOLOv5s-Seg model. The small leaves of early-stage luffa seedlings are liable to be mistaken for impurities in the plug trays. To address this issue, cross-scale connections and weighted feature fusion are introduced in order to integrate feature information from different levels, thereby improving the recognition and segmentation accuracy of seedlings or details by refining the PANet structure. To address the ambiguity of seedling edge information during segmentation, an efficient channel attention module is incorporated to enhance the network’s focus on seedling edge information and suppress irrelevant features, thus sharpening the model’s focus on luffa seedlings. By optimizing the CIoU loss function, the calculation of overlapping areas, center point distances, and aspect ratios between predicted and ground truth boxes is preserved, thereby accelerating the convergence process and reducing the computational resource requirements on edge devices. The experimental results demonstrate that the proposed model attains a mean average precision of 97.03% on a self-compiled luffa plug seedling dataset, representing a 6.23 percentage point improvement over the original YOLOv5s-Seg. Furthermore, compared to the YOLACT++, FCN, and Mask R-CNN segmentation models, the improved model displays increases in mAP@0.5 of 12.93%, 13.73%, and 10.53%, respectively, and improvements in precision of 15.73%, 16.93%, and 13.33%, respectively. This research not only validates the viability of the enhanced model for luffa seedling grading but also provides tangible technical support for the automation of grading in agricultural production. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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19 pages, 6491 KiB  
Article
Identification and Location Method of Bitter Gourd Picking Point Based on Improved YOLOv5-Seg
by Sheng Jiang, Yechen Wei, Shilei Lyu, Hualin Yang, Ziyi Liu, Fangnan Xie, Jiangbo Ao, Jingye Lu and Zhen Li
Agronomy 2024, 14(10), 2403; https://doi.org/10.3390/agronomy14102403 - 17 Oct 2024
Cited by 1 | Viewed by 1042
Abstract
Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention [...] Read more.
Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention (CA) mechanism module, and combines it with a refinement algorithm to identify and locate the picking points of bitter gourd. Firstly, the improved algorithm model was used to identify and segment bitter gourd and melon stems. Secondly, the melon stem mask was extracted, and the thinning algorithm was used to refine the skeleton of the extracted melon stem mask image. Finally, a skeleton refinement graph of bitter gourd stem was traversed, and the midpoint of the largest connected region was selected as the picking point of bitter gourd. The experimental results show that the prediction precision (P), precision (R) and mean average precision (mAP) of the improved YOLOv5-seg model in object recognition were 98.04%, 97.79% and 98.15%, respectively. Compared with YOLOv5-seg, the P, R and mA values were increased by 2.91%, 4.30% and 1.39%, respectively. In terms of object segmentation, mask precision (P(M)) was 99.91%, mask recall (R(M)) 99.89%, and mask mean average precision (mAP(M)) 99.29%. Compared with YOLOv5-seg, the P(M), R(M), and mAP(M) values were increased by 6.22%, 7.81%, and 5.12%, respectively. After testing, the positioning error of the three-dimensional coordinate recognition of bitter gourd picking points was X-axis = 7.025 mm, Y-axis =5.6135 mm, and Z-axis = 11.535 mm, and the maximum allowable error of the cutting mechanism at the end of the picking manipulator was X-axis = 30 mm, Y-axis = 24.3 mm, and Z-axis = 50 mm. Therefore, this results of study meet the positioning accuracy requirements of the cutting mechanism at the end of the manipulator. The experimental data show that the research method in this paper has certain reference significance for the accurate identification and location of bitter gourd picking points. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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12 pages, 1288 KiB  
Article
Effects of Spray Adjuvants on Droplet Deposition Characteristics in Litchi Trees under UAV Spraying Operations
by Xiaonan Wang, Yanping Liu, Shilin Wang and Siwei Wang
Agronomy 2024, 14(9), 2125; https://doi.org/10.3390/agronomy14092125 - 18 Sep 2024
Cited by 4 | Viewed by 1130
Abstract
In the last decade, unmanned aerial vehicles (UAVs) for plant protection have rapidly developed worldwide as a new method for pesticide application, especially in China and other Asian countries. To improve the deposition quality in UAV applications, adding appropriate types of spray adjuvants [...] Read more.
In the last decade, unmanned aerial vehicles (UAVs) for plant protection have rapidly developed worldwide as a new method for pesticide application, especially in China and other Asian countries. To improve the deposition quality in UAV applications, adding appropriate types of spray adjuvants into pesticide solutions is one of the most effective ways to facilitate droplet deposition and control efficacy. At present, research on spray adjuvants for UAVs are mainly based on droplet drift and laboratory tests. Few studies have been conducted on the physicochemical properties of spray adjuvants and the effects of droplet deposition characteristics. To explore the properties of four different kinds of spray adjuvants (Mai Fei, Bei Datong, G-2801, and Agrospred 910) and the deposition characteristics of spray adjuvants on litchi leaves, an automatic surface tension meter, a contact angle measuring device, an ultraviolet visible spectrophotometer, and a DJI AGRAS T30 plant protection UAV was used to measure the surface tension, contact angle, and droplet deposition characteristics on litchi under UAV spraying operations. The results showed that the addition of spray adjuvants could significantly reduce the surface tension of the solution. The surface tension value of the solution after adding the spray additives was reduced by 53.1–68.9% compared with the control solution. Among them, the Agrospred 910 spray adjuvant had the best effect on reducing the surface tension of the solution. The contact angle of the control solution on the litchi leaves varied from 80.15° to 72.76°. With the increase in time, the contact angle of the spray adjuvant solution gradually decreased, the Agrospred 910 spray adjuvant had the best effect, and the contact angle decreased from 40.44° to 20.23° after the droplets fell on the litchi leaves for 60 s. The adjuvant solutions increased the droplet size, but the uniformity of the droplet size decreased. The Dv0.5 of different spray solutions ranged from 97.3 to 117.8 μm, which belonged to the fine or very fine droplets, and the Dv0.5 of adjuvants solutions were significantly greater than that of the control solution. The RSs of adjuvant solutions were very similar and ranged from 0.92 to 0.96, all of which were significantly greater than the result of the control solution (0.57). Compared with the deposition of the control solution, the Mai Fei, Bei Datong, and G-2801 solutions clearly increased spray deposition, with total depositions of 0.776, 0.705, and 0.721 μL/cm2, which are all greater than the total deposition of the control solution of 0.645 μL/cm2. The addition of tank-mixed adjuvants could effectively increase the uniformity of the spray deposition, and all the average CVs of adjuvant solutions were lower than 96.86%. On the whole, Mai Fei performed best in increasing the spray deposition and promoting penetration, followed by Bei Datong and G-2801. Meanwhile, the test can also provide a reference for improving the utilization rate of UAV pesticide applications. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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14 pages, 2320 KiB  
Article
Optimization of Application Parameters for UAV-Based Liquid Pollination in Pear Orchards: A Yield and Cost Perspective
by Pei Wang, Moxin He, Mingqi Li, Yuheng Yang, Hui Li, Wanpeng Xi and Tong Zhang
Agronomy 2024, 14(9), 2033; https://doi.org/10.3390/agronomy14092033 - 6 Sep 2024
Cited by 1 | Viewed by 1050
Abstract
Unmanned aerial vehicle (UAV) liquid pollination emerges as a promising substitute for hand pollination methods. In this study, the relationship between UAV liquid pollination and fruit thinning operations was explored from the perspective of practical application. By testing droplet deposition under various flight [...] Read more.
Unmanned aerial vehicle (UAV) liquid pollination emerges as a promising substitute for hand pollination methods. In this study, the relationship between UAV liquid pollination and fruit thinning operations was explored from the perspective of practical application. By testing droplet deposition under various flight parameters, the flight parameters for a specific pear orchard were optimized to ensure the uniform and effective distribution of the pollination solution. Results indicated that optimal droplet density (number·cm−2), area coverage (%), and deposition rate (μL·cm−2) were achieved at a flight height (FH) of 1.5 m and a flight speed (FS) of 2 m·s−1. Considering the nuanced physiological attributes of pear tree flowers during their pollination phase, the research scrutinizes the impact of application parameters such as floral stage and spraying frequency on pollination efficiency. A two-way ANOVA analysis demonstrated significant impacts of floral stage, spraying frequency, and their interaction on the fruit set rate (p < 0.01). Controlling pollination parameters can effectively regulate the fruit set rate, thereby influencing the cost and efficiency of fruit thinning. These findings contribute a theoretical framework for formulating customized pollination management strategies tailored to the specific needs of pear orchards. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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27 pages, 11164 KiB  
Article
Design and Development of a Side Spray Device for UAVs to Improve Spray Coverage in Obstacle Neighborhoods
by Fanrui Kong, Baijing Qiu, Xiaoya Dong, Kechuan Yi, Qingqing Wang, Chunxia Jiang, Xinwei Zhang and Xin Huang
Agronomy 2024, 14(9), 2002; https://doi.org/10.3390/agronomy14092002 - 2 Sep 2024
Cited by 5 | Viewed by 1298
Abstract
Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid [...] Read more.
Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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17 pages, 7699 KiB  
Article
Design and Test of Novel Uniform Application Equipment with Nozzles Swinging Horizontally Used for UAVs
by Shuangshuang Wang, Han Zhang, Guozhong Zhang and Haopeng Liu
Agronomy 2024, 14(8), 1631; https://doi.org/10.3390/agronomy14081631 - 25 Jul 2024
Viewed by 1050
Abstract
Given the problems such as insufficient control on pests and diseases or pesticide damage on plants caused by uneven distribution of pesticide droplets during the current application process by UAVs, this paper designed novel uniform application equipment with nozzles swinging horizontally based on [...] Read more.
Given the problems such as insufficient control on pests and diseases or pesticide damage on plants caused by uneven distribution of pesticide droplets during the current application process by UAVs, this paper designed novel uniform application equipment with nozzles swinging horizontally based on a UAV platform in order to improve the distribution uniformity of droplets volume. Nozzles swinging periodically are able to increase the overlap probability of spray fans generated from nozzles. It is helpful to further the spray deposition uniformity improvement. Through droplet motion analysis, CFD simulation, and spray tests, it was determined that the key factors affecting uniformity were the oscillating rod length, spray height, and nozzle angle. The best parameter combination was explored as the length of 175 mm, the height of 1.5 m, and the angle of 15°. Based on this combination, the prototype was produced and installed on the UAV platform. A field test was carried out to verify its performance. The results showed that the CV of the improved UAV was 26.41%, which was 6.43 percentage points lower than the traditional UAV, and the decrease was 19.58%, meaning that it is feasible to use this equipment to improve uniformity. Full article
(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)
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