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Agriculture
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Agricultural Collaborative Robots for Smart Farming
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Agricultural Collaborative Robots for Smart Farming

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8148

Special Issue Editors

Prof. Dr. Bin Xie

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Guest Editor
College of Engineering, China Agricultural University, Beijing, China
Interests: electric agricultural vehicle; agricultural robot; electric tractor; intelligent control and optimization; automatic navigation; artificial intelligence in agriculture
Prof. Dr. Zhijun Meng

E-Mail Website
Guest Editor
National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing, China
Interests: agricultural machinery automatic navigation; agricultural robots; precision operation control; agricultural machinery big data on; smart farming
Prof. Dr. Jun Zhou

E-Mail Website
Guest Editor
College of Engineering, Nanjing Agricultural University, Nanjing, China
Interests: agricultural robotics; agricultural intelligent technology and equipment; machine vision; precision agriculture equipment

Special Issue Information

Dear Colleagues,

The world population is increasing annually, and the demand for quantity, quality, and safety of food is also increasing, putting higher requirements on the scale, integration, automation, and intelligence of the world's agricultural production. In the background of an aging global population and seasonal manpower shortages, agricultural robotics shows great potential for application. At present, a number of agricultural robots for independent execution of specific tasks (e.g., plant protection, monitoring, feeding, harvesting, etc.) have been developed and are well applied. This inspires us to continue our research on collaborative agricultural robots to further improve the efficiency and intelligence of agricultural production.

This special issue aims to introduce the application of collaborative agricultural robots in smart farming. Topics of interest include but are not limited to: human-robot cooperation in modern agricultural scenarios (collaboration theory, interaction methods, etc.), collaborative unmanned aerial vehicles (UAVs) for livestock monitoring, collaborative unmanned ground vehicles (UGVs) for harvesting/transportation, collaboration between UAVs and UGVs for plant protection, Multi-arm collaborative robot for fruit and vegetable picking. Welcome original research articles and reviews.

Prof. Dr. Bin Xie
Prof. Dr. Zhijun Meng
Prof. Dr. Jun Zhou
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

  • agricultural robots
  • collaborative Operations
  • human-robot collaboration
  • UGV and UAV
  • multiple UAV
  • multiple UGV
  • robotic arm

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Published Papers (6 papers)

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Research

17 pages, 29659 KiB  
Article
Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation
by Jaehwi Seol, Yonghyun Park, Jeonghyeon Pak, Yuseung Jo, Giwan Lee, Yeongmin Kim, Chanyoung Ju, Ayoung Hong and Hyoung Il Son
Agriculture 2024, 14(11), 1985; https://doi.org/10.3390/agriculture14111985 - 5 Nov 2024
Viewed by 523
Abstract
This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications [...] Read more.
This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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14 pages, 12763 KiB  
Article
Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting
by Qian Wang, Wuchang Qin, Mengnan Liu, Junjie Zhao, Qingzhen Zhu and Yanxin Yin
Agriculture 2024, 14(10), 1846; https://doi.org/10.3390/agriculture14101846 - 19 Oct 2024
Viewed by 756
Abstract
The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to [...] Read more.
The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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17 pages, 8934 KiB  
Article
Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques
by Kaiting Xie, Zhaoguo Zhang and Shiliang Zhu
Agriculture 2024, 14(8), 1396; https://doi.org/10.3390/agriculture14081396 - 18 Aug 2024
Viewed by 864
Abstract
This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses [...] Read more.
This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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22 pages, 15768 KiB  
Article
Human–Robot Skill Transferring and Inverse Velocity Admittance Control for Soft Tissue Cutting Tasks
by Kaidong Liu, Bin Xie, Zhouyang Chen, Zhenhao Luo, Shan Jiang and Zhen Gao
Agriculture 2024, 14(3), 394; https://doi.org/10.3390/agriculture14030394 - 29 Feb 2024
Viewed by 1272
Abstract
Robotic meat cutting is increasingly in demand in meat industries due to safety issues, labor shortages, and inefficiencies. This paper proposes a multi-demonstration human–robot skill transfer framework to address the flexible and generalized cutting of sheep hindquarters with complex 3D anatomy structures by [...] Read more.
Robotic meat cutting is increasingly in demand in meat industries due to safety issues, labor shortages, and inefficiencies. This paper proposes a multi-demonstration human–robot skill transfer framework to address the flexible and generalized cutting of sheep hindquarters with complex 3D anatomy structures by imitating humans. To improve the generalization with meat sizes and demonstrations and extract target cutting behaviors, multi-demonstrations of cutting are encoded into low-dimension latent space through principal components analysis (PCA), Gaussian mixture model (GMM), and Gaussian mixture regression (GMR). To improve the robotic cutting flexibility and the cutting behavior reproducing accuracy, this study combines a modified dynamic movement primitive (DMP) high-level behavior generator with the low-level joints admittance control (AC) through real-time inverse velocity (IV) kinematics solving and constructs the IVAC-DMP control module. The experimental results show that the maximum residual meat thickness in the sheep hindquarter cutting of sample 1 is 3.1 mm, and sample 2 is 3.8 mm. The residual rates of samples 1 and 2 are 5.6% and 4.8%. Both meet the requirements for sheep hindquarter separation. The proposed framework is advantageous for harvesting high-value meat products and providing a reference technique for robot skill learning in interaction tasks. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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17 pages, 5087 KiB  
Article
Optimized Design of Robotic Arm for Tomato Branch Pruning in Greenhouses
by Yuhang Ma, Qingchun Feng, Yuhuan Sun, Xin Guo, Wanhao Zhang, Bowen Wang and Liping Chen
Agriculture 2024, 14(3), 359; https://doi.org/10.3390/agriculture14030359 - 23 Feb 2024
Cited by 1 | Viewed by 1496
Abstract
Aiming at the robotic pruning of tomatoes in greenhouses, a new PRRPR configuration robotic arm consisting of two prismatic (P) joints and three revolute (R) joints was designed to locate the end effector to handle randomly growing branches with an appropriate posture. In [...] Read more.
Aiming at the robotic pruning of tomatoes in greenhouses, a new PRRPR configuration robotic arm consisting of two prismatic (P) joints and three revolute (R) joints was designed to locate the end effector to handle randomly growing branches with an appropriate posture. In view of the various spatial posture of the branches, drawing on the skill of manual pruning operation, we propose a description method of the optimal operation posture of the pruning end effector, proposing a method of solving the inverse kinematics of the pruning arm based on the multi-objective optimization algorithm. According to the spatial distribution characteristics of the tomato branches along the main stem, the robotic arm structure is compact and the reachable space is maximized as the objective function, and a method of optimizing the key geometric parameters of the robotic arm is proposed. The optimal maximum length of the arm’s horizontal slide joint was determined to be 953.149 mm and the extension maximum length of its telescopic joint was 632.320 mm. The verification test of the optimal structural parameter showed that the optimized robotic arm could reach more than 89.94% of the branches in the pruning target area with a posture that meets the pruning requirements. This study is supposed to provide technical support for the development of a tomato pruning robot. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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16 pages, 4079 KiB  
Article
Headland Identification and Ranging Method for Autonomous Agricultural Machines
by Hui Liu, Kun Li, Luyao Ma and Zhijun Meng
Agriculture 2024, 14(2), 243; https://doi.org/10.3390/agriculture14020243 - 1 Feb 2024
Cited by 2 | Viewed by 1358
Abstract
Headland boundary identification and ranging are the key supporting technologies for the automatic driving of intelligent agricultural machinery, and they are also the basis for controlling operational behaviors such as autonomous turning and machine lifting. The complex, unstructured environments of farmland headlands render [...] Read more.
Headland boundary identification and ranging are the key supporting technologies for the automatic driving of intelligent agricultural machinery, and they are also the basis for controlling operational behaviors such as autonomous turning and machine lifting. The complex, unstructured environments of farmland headlands render traditional image feature extraction methods less accurate and adaptable. This study utilizes deep learning and binocular vision technologies to develop a headland boundary identification and ranging system built upon the existing automatic guided tractor test platform. A headland image annotation dataset was constructed, and the MobileNetV3 network, notable for its compact model structure, was employed to achieve binary classification recognition of farmland and headland images. An improved MV3-DeeplabV3+ image segmentation network model, leveraging an attention mechanism, was constructed, achieving a high mean intersection over union (MIoU) value of 92.08% and enabling fast and accurate detection of headland boundaries. Following the detection of headland boundaries, binocular stereo vision technology was employed to measure the boundary distances. Field experiment results indicate that the system’s average relative errors of distance in ranging at distances of 25 m, 20 m, and 15 m are 6.72%, 4.80%, and 4.35%, respectively. This system is capable of meeting the real-time detection requirements for headland boundaries. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
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