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Agriculture
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Perception, Decision-Making, and Control of Agricultural Robots
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Perception, Decision-Making, and Control of Agricultural Robots

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 5972

Special Issue Editors

Dr. Jinlin Sun

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: agricultural robots; unmanned agricultural machinery; anti-disturbance control; adaptive control; intelligent optimization algorithm; sliding mode control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shihong Ding

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: unmanned agricultural machinery; sliding mode control; nonlinear control; cooperative control; applications of mechatronic engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture has always been the foundation of human civilization. In recent decades, with the continuous growth in the global population, the demand for food has increased. Accordingly, the requirements for productivity and labor force in agricultural operations have also expanded. This poses significant challenges to agricultural production. Meanwhile, the issue of global agricultural labor shortage is becoming increasingly severe, especially in some developed countries where labor costs continue to escalate. In addition, climate change and resource scarcity also present challenges for traditional agricultural patterns.

Fortunately, with the continuous development of technology, the future of agriculture is increasingly closely linked with the application of agricultural robots. As an important part of future productive forces, agricultural robots can not only improve production efficiency and reduce labor costs, but also enhance the quality of agricultural products by precise unmanned operations such as planting, fertilizing, weeding, and harvesting. In order to improve the performance of agricultural robots, main technologies involving perception, decision-making, and control are becoming more critical. These technologies will enable robots to adapt to complex field environments, ensuring precise and efficient agricultural production.

This Special Issue aims to explore the latest research progress in the field of agricultural robots regarding their perception, decision-making, and control, providing a platform for researchers and practitioners to share their insights and experiences. We look forward to further promoting the development of agricultural robotics with these studies so that they can better contribute to global agricultural production. We warmly welcome contributions from researchers and practitioners sharing their latest innovations and practical experiences in agricultural robots.

Dr. Jinlin Sun
Prof. Dr. Shihong Ding
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

  • agricultural robots
  • unmanned agricultural machinery
  • intelligent control
  • unmanned field operation
  • intelligent perception
  • agricultural navigation systems
  • intelligent decision-making
  • efficient agricultural production

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Published Papers (5 papers)

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Research

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22 pages, 6589 KB  
Article
Research on Variable-Rate Spray Control System Based on Improved ANFIS
by Derui Bao, Changxi Liu, Yufei Li, Hang Shi, Chuang Yan, Hang Xue and Jun Hu
Agriculture 2025, 15(24), 2607; https://doi.org/10.3390/agriculture15242607 - 17 Dec 2025
Viewed by 195
Abstract
To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural [...] Read more.
To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural Fuzzy Inference System (ANFIS). Flow characteristic data of the solenoid valve were collected under four pressure conditions (0.2–0.5 MPa), drive frequencies (5–20 Hz), and duty cycles (10–90%) using an indoor test system. An ANFIS controller architecture was constructed with target flow rate and actual travel speed as input variables and PWM frequency-duty cycle combinations as output variables. This controller enhances the traditional single-output mode of ANFIS by achieving multi-output collaborative optimization through shared premise parameters, thereby strengthening the system’s nonlinear modeling and control capabilities. To validate the system’s practical performance, a field simulation test platform based on a spraying robot was constructed. By analyzing preset prescription map information, the system achieved precise variable-rate spraying operations during movement. Test results demonstrate that the steady-state error remains within 5.03% under various speed-varying conditions. This research provides a high-precision intelligent control solution for variable-rate spraying systems, holding significant implications for reducing pesticide application rates and advancing precision agriculture. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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19 pages, 4432 KB  
Article
Enhanced YOLOv5 with ECA Module for Vision-Based Apple Harvesting Using a 6-DOF Robotic Arm in Occluded Environments
by Yan Xu, Xuejie Qiao, Li Ding, Xinghao Li, Zhiyu Chen and Xiang Yue
Agriculture 2025, 15(17), 1850; https://doi.org/10.3390/agriculture15171850 - 29 Aug 2025
Viewed by 967
Abstract
Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based [...] Read more.
Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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25 pages, 4021 KB  
Article
A Hybrid Path Planning Algorithm for Orchard Robots Based on an Improved D* Lite Algorithm
by Quanjie Jiang, Yue Shen, Hui Liu, Zohaib Khan, Hao Sun and Yuxuan Huang
Agriculture 2025, 15(15), 1698; https://doi.org/10.3390/agriculture15151698 - 6 Aug 2025
Viewed by 1594
Abstract
Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path [...] Read more.
Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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Review

Jump to: Research

43 pages, 7882 KB  
Review
Path Tracking Control in Autonomous Agricultural Vehicles: A Systematic Survey of Models, Methods, and Challenges
by Chuanhao Sun, Jinlin Sun, Shihong Ding, Qiushi Li and Li Ma
Agriculture 2025, 15(23), 2522; https://doi.org/10.3390/agriculture15232522 - 4 Dec 2025
Viewed by 565
Abstract
With the advancement of precision agriculture and agriculture 4.0, path tracking control technologies for autonomous agricultural vehicles (AAVs) have become essential for achieving efficient and automated operations. This paper begins by introducing the theoretical framework of AAV path tracking, including its applications across [...] Read more.
With the advancement of precision agriculture and agriculture 4.0, path tracking control technologies for autonomous agricultural vehicles (AAVs) have become essential for achieving efficient and automated operations. This paper begins by introducing the theoretical framework of AAV path tracking, including its applications across various working scenarios such as dry fields, paddy fields, and orchards, and establishes corresponding vehicle dynamics models suited to these environments. AAVs are classified into wheeled and tracked types based on structural characteristics and specific operational requirements. Subsequently, path tracking control methods are divided into linear and nonlinear approaches according to their system applicability, with detailed discussions on the implementation and adaptations of these strategies in real agricultural settings. Given its strong robustness and extensive adoption, sliding mode control receives particular emphasis in this review. Finally, the paper addresses persistent challenges in complex farmland environments and identifies future research directions aimed at enhancing practicality and adaptability. This review provides a comprehensive and structured analysis of AAV path tracking technologies, with a focus on environmental adaptability and operational feasibility, thereby offering valuable insights for further research and technological development in precision agriculture. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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41 pages, 2786 KB  
Review
Research Status and Development Trends of Artificial Intelligence in Smart Agriculture
by Chuang Ge, Guangjian Zhang, Yijie Wang, Dandan Shao, Xiangjin Song and Zhaowei Wang
Agriculture 2025, 15(21), 2247; https://doi.org/10.3390/agriculture15212247 - 28 Oct 2025
Viewed by 1702
Abstract
Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, [...] Read more.
Artificial Intelligence (AI) is a key technological enabler for the transition of agricultural production and management from experience-driven to data-driven, continuously advancing modern agriculture toward smart agriculture. This evolution ultimately aims to achieve a precise agricultural production model characterized by low resource consumption, high safety, high quality, high yield, and stable, sustainable development. Although machine learning, deep learning, computer vision, Internet of Things, and other AI technologies have made significant progress in numerous agricultural production applications, most studies focus on singular agricultural scenarios or specific AI algorithm research, such as object detection, navigation, agricultural machinery maintenance, and food safety, resulting in relatively limited coverage. To comprehensively elucidate the applications of AI in agriculture and provide a valuable reference for practitioners and policymakers, this paper reviews relevant research by investigating the entire agricultural production process—including planting, management, and harvesting—covering application scenarios such as seed selection during the cultivation phase, pest and disease identification and intelligent management during the growth phase, and agricultural product grading during the harvest phase, as well as agricultural machinery and devices like fault diagnosis and predictive maintenance of agricultural equipment, agricultural robots, and the agricultural Internet of Things. It first analyzes the fundamental principles and potential advantages of typical AI technologies, followed by a systematic and in-depth review of the latest progress in applying these core technologies to smart agriculture. The challenges faced by existing technologies are also explored, such as the inherent limitations of AI models—including poor generalization capability, low interpretability, and insufficient real-time performance—as well as the complex agricultural operating environments that result in multi-source, heterogeneous, and low-quality, unevenly annotated data. Furthermore, future research directions are discussed, such as lightweight network models, transfer learning, embodied intelligent agricultural robots, multimodal perception technologies, and large language models for agriculture. The aim is to provide meaningful insights for both theoretical research and practical applications of AI technologies in agriculture. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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