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Agriculture
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Intelligent Robots for Agriculture: Design, Development and Applications
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Intelligent Robots for Agriculture: Design, Development and Applications

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 12324

Special Issue Editors

Prof. Dr. Hongjun Wang

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural robot; intelligent design and manufacturing; system simulation; intelligent design and virtual design; computer vision
Dr. Hanwen Kang

E-Mail Website
Guest Editor
College of Engineering, South China Agriculture University, Guangzhou 510070, China
Interests: robotics; robotic vision; deep learning; autonomous navigation; field robot; robot planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juntao Xiong

E-Mail Website
Guest Editor
School of Mathematics and Information Science, South China Agricultural University, Guangzhou 510642, China
Interests: agricultural robot; intelligent design and manufacturing; machine vision; agricultural informatization; big data analysis and decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional agricultural production methods face challenges such as high labor costs, low efficiency, and unequal resource utilization. With the emergence of automation and robotics technology, the agricultural industry has undergone significant transformations, taking solid steps towards precision agriculture, achieving high resource utilization efficiency, and sustainable production techniques. The application of intelligent robots in agriculture has also become a prominent research area.

The Special Issue will delve into the growing significance of intelligent robots in agriculture, covering the customization, development, and implementation of robotic systems for diverse agricultural activities. It will offer thorough insight into the potential influence of robotic technologies for streamlining agricultural processes, boosting productivity, and tackling farming sector challenges.

By integrating advanced technologies such as automation, intelligent manufacturing, and artificial intelligence, intelligent robots can achieve precision and automation in field operations, enhancing production efficiency, thus reducing farmers' labor intensity. This approach also contributes to sustainable agricultural development goals and promotes the modernization of agriculture.

This Special Issue will provide suggestions and new perspectives to promote the transformation and upgrading of the agricultural industry and improve agricultural production methods. It covers areas such as intelligent manufacturing, agricultural science, artificial intelligence, and mechanical manufacturing. We seek original research articles, reviews, and case studies that present novel developments in the design, implementation, and real-world applications of intelligent robots for agriculture.

Prof. Dr. Hongjun Wang
Dr. Hanwen Kang
Prof. Dr. Juntao Xiong
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. 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

  • horticulture
  • field robotics
  • machine learning
  • autonomous navigation
  • robotic manipulation
  • teleoperation
  • human–robot interaction
  • UAV application
  • mechanical design
  • precision agriculture
  • agricultural intelligent system
  • intelligent design and manufacturing

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Published Papers (9 papers)

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Research

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14 pages, 5129 KiB  
Article
Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV
by Hasib Mansur, Manoj Gadhwal, John Eric Abon and Daniel Flippo
Agriculture 2025, 15(8), 882; https://doi.org/10.3390/agriculture15080882 - 18 Apr 2025
Viewed by 291
Abstract
Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop [...] Read more.
Mapping is fundamental to the autonomous navigation of agricultural robots, as it provides a comprehensive spatial understanding of the farming environment. Accurate maps enable robots to plan efficient routes, avoid obstacles, and precisely execute tasks such as planting, spraying, and harvesting. Row crop navigation presents unique challenges, and mapping plays a crucial role in optimizing routes and avoiding obstacles in coverage path planning (CPP), which is essential for efficient agricultural operations. This study proposes a simple method for using Unmanned Aerial Vehicles (UAVs) to create maps and its application to row crop navigation. A case study is presented to demonstrate the method’s viability and illustrate how the resulting map can be applied in agricultural scenarios. This study focused on two major row crops, namely corn and soybean, but the results indicate that map creation is feasible when the inter-row spaces are not obscured by canopy cover from the adjacent rows. Although the study did not apply the map in a real-world scenario, it offers valuable insights for guiding future research. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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26 pages, 14214 KiB  
Article
Stereo Visual Odometry and Real-Time Appearance-Based SLAM for Mapping and Localization in Indoor and Outdoor Orchard Environments
by Imran Hussain, Xiongzhe Han and Jong-Woo Ha
Agriculture 2025, 15(8), 872; https://doi.org/10.3390/agriculture15080872 - 16 Apr 2025
Viewed by 225
Abstract
Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an [...] Read more.
Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an increased risk of collisions affect the robot’s ability to perform tasks such as fruit harvesting, spraying, and monitoring. To address these limitations, this study integrated stereo visual odometry with real-time appearance-based mapping (RTAB-Map)-based simultaneous localization and mapping (SLAM) to improve mapping and localization in both indoor and outdoor orchard settings. The proposed system leverages stereo image pairs for precise depth estimation while utilizing RTAB-Map’s graph-based SLAM framework with loop-closure detection to ensure global map consistency. In addition, an incorporated inertial measurement unit (IMU) enhances pose estimation, thereby improving localization accuracy. Substantial improvements in both mapping and localization performance over the traditional approach were demonstrated, with an average error of 0.018 m against the ground truth for outdoor mapping and a consistent average error of 0.03 m for indoor trails with a 20.7% reduction in visual odometry trajectory deviation compared to traditional methods. Localization performance remained robust across diverse conditions, with a low RMSE of 0.207 m. Our approach provides critical insights into developing more reliable autonomous navigation systems for agricultural robots. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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23 pages, 4421 KiB  
Article
Study on Predicting Blueberry Hardness from Images for Adjusting Mechanical Gripper Force
by Hao Yin, Wenxin Li, Han Wang, Yuhuan Li, Jiang Liu and Baogang Li
Agriculture 2025, 15(6), 603; https://doi.org/10.3390/agriculture15060603 - 11 Mar 2025
Viewed by 464
Abstract
Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method [...] Read more.
Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method based on machine vision. The method uses image recognition technology to extract the physical characteristics of blueberries, such as diameter and thickness, and estimates fruit hardness in real-time through a predictive model. The gripping force of the mechanical claw is dynamically adjusted to ensure non-destructive harvesting. Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. Next, an orthogonal experiment further verified the model, showing that the prediction error between the model’s values and actual values was less than 5%. Additionally, considering practical applications, a simple and efficient two-parameter method was proposed, removing the weight parameter and predicting fruit hardness using only diameter and thickness. Although the two-parameter method increases the prediction error by 0.36% compared to the three-parameter method, it reduces the number of convergence steps by 71 and shortens the computation time by one-third, significantly improving iteration speed. Finally, further crushing experiments showed that using the two-parameter method for hardness prediction through parameter extraction via visual recognition resulted in a relative error of less than 8%, with an average relative error of 3.91%. The error falls within the acceptable range for the safety factor design. This method provides a novel solution for the non-damaging mechanized harvesting of soft fruits. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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22 pages, 51155 KiB  
Article
Development and Experiment of Adaptive Oolong Tea Harvesting Robot Based on Visual Localization
by Ruidong Yu, Yinhui Xie, Qiming Li, Zhiqin Guo, Yuanquan Dai, Zhou Fang and Jun Li
Agriculture 2024, 14(12), 2213; https://doi.org/10.3390/agriculture14122213 - 3 Dec 2024
Cited by 2 | Viewed by 1025
Abstract
Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an adaptive oolong tea harvesting robot with an adjustment module of a cutting tool and a harvesting line localization algorithm is proposed. The robot includes a vision measurement [...] Read more.
Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an adaptive oolong tea harvesting robot with an adjustment module of a cutting tool and a harvesting line localization algorithm is proposed. The robot includes a vision measurement module and an adjustment mechanism of a cutting tool, enabling it to assess the shape of tea bushes and adaptively adjust the cutter configuration. To address the challenges of complex tea bush structures and environmental noise, a Prior–Tukey RANSAC algorithm was proposed for accurate harvesting model fitting. Our algorithm leverages prior knowledge about tea bush stem characteristics, uses the Tukey loss function to enhance robustness to outliers, and incorporates workspace constraints to ensure that the cutting tool remains within feasible operational limits. To evaluate the performance of the robot, experiments were conducted in a tea garden in Wuyi Mountain, China. Under ideal conditions, our algorithm achieved an inlier ratio of 43.10% and an R2 value of 0.9787, significantly outperforming traditional RANSAC and other variants. Under challenging field conditions, the proposed algorithm demonstrated robustness, maintaining an inlier ratio of 47.50% and an R2 value of 0.9598. And the processing time of the algorithm met the real-time requirements for effective tea-picking operations. The field experiments also showed an improvement in intact tea rates, from 79.34% in the first harvest to 81.57% in the second harvest, with a consistent usable tea rate of around 85%. Additionally, the robot had a harvesting efficiency of 260.14 kg/h, which was superior to existing handheld and riding-type tea pickers. These results indicate that the robot effectively balances efficiency, accuracy, and robustness, providing a promising solution for high-quality tea harvesting in complex environments. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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21 pages, 7978 KiB  
Article
Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data
by Phummarin Thavitchasri, Dechrit Maneetham and Padma Nyoman Crisnapati
Agriculture 2024, 14(9), 1557; https://doi.org/10.3390/agriculture14091557 - 9 Sep 2024
Cited by 1 | Viewed by 1186
Abstract
This study aims to enhance the navigation capabilities of autonomous tractors by predicting the surface type they are traversing using data collected from BNO055 Inertial Measurement Units (IMU sensors). IMU sensor data were collected from a small mobile robot driven over seven different [...] Read more.
This study aims to enhance the navigation capabilities of autonomous tractors by predicting the surface type they are traversing using data collected from BNO055 Inertial Measurement Units (IMU sensors). IMU sensor data were collected from a small mobile robot driven over seven different floor surfaces within a university environment, including tile, carpet, grass, gravel, asphalt, concrete, and sand. Several machine learning models, including Logistic Regression, K-Neighbors, SVC, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and XGBoost, were trained and evaluated to predict the surface type based on the sensor data. The results indicate that Random Forest and XGBoost achieved the highest accuracy, with scores of 98.5% and 98.7% in K-Fold Cross-Validation, respectively, and 98.8% and 98.6% in an 80/20 Random State split. These findings demonstrate that ensemble methods are highly effective for this classification task. Accurately identifying surface types can prevent operational errors and improve the overall efficiency of autonomous systems. Integrating these models into autonomous tractor systems can significantly enhance adaptability and reliability across various terrains, ensuring safer and more efficient operations. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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20 pages, 11079 KiB  
Article
Development, Integration, and Field Experiment Optimization of an Autonomous Banana-Picking Robot
by Tianci Chen, Shiang Zhang, Jiazheng Chen, Genping Fu, Yipeng Chen and Lixue Zhu
Agriculture 2024, 14(8), 1389; https://doi.org/10.3390/agriculture14081389 - 17 Aug 2024
Cited by 1 | Viewed by 1435
Abstract
The high growth height and substantial weight of bananas present challenges for robots to harvest autonomously. To address the issues of high labor costs and low efficiency in manual banana harvesting, a highly autonomous and integrated banana-picking robot is proposed to achieve autonomous [...] Read more.
The high growth height and substantial weight of bananas present challenges for robots to harvest autonomously. To address the issues of high labor costs and low efficiency in manual banana harvesting, a highly autonomous and integrated banana-picking robot is proposed to achieve autonomous harvesting of banana bunches. A prototype of the banana-picking robot was developed, featuring an integrated end-effector capable of clamping and cutting tasks on the banana stalks continuously. To enhance the rapid and accurate identification of banana stalks, a target detection vision system based on the YOLOv5s deep learning network was developed. Modules for detection, positioning, communication, and execution were integrated to successfully develop a banana-picking robot system, which has been tested and optimized in multiple banana plantations. Experimental results show that this robot can continuously harvest banana bunches. The average precision of detection is 99.23%, and the location accuracy is less than 6 mm. The robot picking success rate is 91.69%, and the average time from identification to harvesting completion is 33.28 s. These results lay the foundation for the future application of banana-picking robots. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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20 pages, 6246 KiB  
Article
YOLOv8n-DDA-SAM: Accurate Cutting-Point Estimation for Robotic Cherry-Tomato Harvesting
by Gengming Zhang, Hao Cao, Yangwen Jin, Yi Zhong, Anbang Zhao, Xiangjun Zou and Hongjun Wang
Agriculture 2024, 14(7), 1011; https://doi.org/10.3390/agriculture14071011 - 26 Jun 2024
Cited by 4 | Viewed by 2473
Abstract
Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point [...] Read more.
Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point due to challenges such as leaves as well as targets that are too small. In this study, we propose a YOLOv8n-DDA-SAM model that adds a semantic segmentation branch to target detection to achieve the desired detection and compute the picking point. To be specific, YOLOv8n is used as the initial model, and a dynamic snake convolutional layer (DySnakeConv) that is more suitable for the detection of the stems of cherry-tomato is used in neck of the model. In addition, the dynamic large convolutional kernel attention mechanism adopted in backbone and the use of ADown convolution resulted in a better fusion of the stem features with the neck features and a certain decrease in the number of model parameters without loss of accuracy. Combined with semantic branch SAM, the mask of picking points is effectively obtained and then the accurate picking point is obtained by simple shape-centering calculation. As suggested by the experimental results, the proposed YOLOv8n-DDA-SAM model is significantly improved from previous models not only in detecting stems but also in obtaining stem’s masks. In the mAP@0.5 and F1-score, the YOLOv8n-DDA-SAM achieved 85.90% and 86.13% respectively. Compared with the original YOLOv8n, YOLOv7, RT-DETR-l and YOLOv9c, the mAP@0.5 has improved by 24.7%, 21.85%, 19.76%, 15.99% respectively. F1-score has increased by 16.34%, 12.11%, 10.09%, 8.07% respectively, and the number of parameters is only 6.37M. In the semantic segmentation branch, not only does it not need to produce relevant datasets, but also improved its mIOU by 11.43%, 6.94%, 5.53%, 4.22% and mAP@0.5 by 12.33%, 7.49%, 6.4%, 5.99% compared to Deeplabv3+, Mask2former, DDRNet and SAN respectively. In summary, the model can well satisfy the requirements of high-precision detection and provides a strategy for the detection system of the cherry-tomato. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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18 pages, 5388 KiB  
Article
Path Planning Algorithm of Orchard Fertilization Robot Based on Multi-Constrained Bessel Curve
by Fanxia Kong, Baixu Liu, Xin Han, Lili Yi, Haozheng Sun, Jie Liu, Lei Liu and Yubin Lan
Agriculture 2024, 14(7), 979; https://doi.org/10.3390/agriculture14070979 - 24 Jun 2024
Cited by 2 | Viewed by 1157
Abstract
Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency [...] Read more.
Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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Review

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37 pages, 7237 KiB  
Review
Classification, Advanced Technologies, and Typical Applications of End-Effector for Fruit and Vegetable Picking Robots
by Chongyang Han, Jinhong Lv, Chengju Dong, Jiehao Li, Yuanqiang Luo, Weibin Wu and Mohamed Anwer Abdeen
Agriculture 2024, 14(8), 1310; https://doi.org/10.3390/agriculture14081310 - 8 Aug 2024
Cited by 1 | Viewed by 3174
Abstract
Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot’s end-effector should be the first part to come into [...] Read more.
Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot’s end-effector should be the first part to come into close contact with the crops. The design and performance requirements of the end-effectors are affected by the fruit and vegetable variety as well as the complexity of unstructured surroundings. This paper summarizes the latest research status of end-effectors for fruit- and vegetable-picking robots. It analyzes the characteristics and functions of end-effectors according to their structural principles and usage, which are classified into clamp, air suction, suction holding, and envelope types. The development and application of advanced technologies, such as the structural design of end-effectors, additional sensors, new materials, and artificial intelligence, were discussed. The typical applications of end-effectors for the picking of different kinds of fruit and vegetables were described, and the advantages, disadvantages, and performance indexes of different end-effectors were given and comparatively analyzed. Finally, challenges and potential future trends of end-effectors for picking robots were reported. This work can be considered a valuable guide to the latest end-effector technology for the design and selection of suitable end-effectors for harvesting different categories of fruit and vegetable crops. Full article
(This article belongs to the Special Issue Intelligent Robots for Agriculture: Design, Development and Applications)
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