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: 15 November 2024 | Viewed by 5273

Special Issue Editors


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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

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

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

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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 (5 papers)

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Research

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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
Viewed by 553
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
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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
Viewed by 661
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
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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 2 | Viewed by 1556
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 [email protected] 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 [email protected] 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 [email protected] 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
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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 1 | Viewed by 691
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
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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
Viewed by 1179
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
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