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Agriculture
Special Issues
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: 15 November 2024 | Viewed by 1307

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 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

  • 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

Published Papers (2 papers)

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Research

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
Viewed by 741
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
(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
Viewed by 322
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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