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Applied Sciences
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Advances in Robotics and Mechatronics for Agriculture
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Advances in Robotics and Mechatronics for Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 14507

Special Issue Editors

Dr. Yunchao Tang

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Guest Editor
College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Interests: computer vision; field robotics; frp composites; recycled concrete; mechanical performance of steel structures; computer vision; 3d reconstruction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chengliang Liu

E-Mail Website
Guest Editor
Institute of Mechatronics & Logistics Equipment, Shanghai Jiao Tong University, Shanghai, China
Interests: agricultural machinery; agricultural automation; field robotics
Prof. Dr. Xiangjun Zou

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou, China
Interests: agricultural machinery design; agricultural automation
Special Issues, Collections and Topics in MDPI journals
Dr. Lufeng Luo

E-Mail Website
Guest Editor
School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China
Interests: robot target recognition; visual cognitive computing; flexible actuator design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The operation scene of agricultural equipment is usually a complex, dynamic, and unstructured environment, with many links in current agricultural production still relying on labor-intensive operation. Flexibility, intelligence, and automation of smart agricultural machine have become a hotspot of modern agriculture at home and abroad. With the sharp development of artificial intelligence, robotics, intelligent sensing, flexible control, and other technologies, these advanced technologies have injected strong power support to improve the intelligent level of agricultural equipment. However, the development of precision agriculture and unmanned farms requires agricultural robots to have a series of intelligent behavior capabilities, including autonomous perception, cognition, autonomous path planning, and flexible adaptive operation.

The main aims of this focused section in Applied Sciences are to present the current state of the art in Robotics and Mechatronics for Precision Agriculture and to illustrate new results in several emerging research areas. Submissions can present theoretical and experimental aspects in these areas. The topics of interest within the scope of this focused section include but are not limited to:

  • Advanced autonomy for unmanned mechatronics systems;
  • Advanced machines or robotics for precision agriculture;
  • Advanced approaches for high-throughput phenotyping and remote sensing;
  • Advancement of cooperative mechatronics systems for precision agriculture;
  • Soft-grasping/soft-robotics manipulators;
  • Fruit/vegetable detection and localization for automatic harvesting robots;
  • Crop yield estimation.

Dr. Yunchan Tang
Prof. Dr. Chengliang Liu
Prof. Dr. Xiangjun Zou
Dr. Lufeng Luo
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. Applied Sciences 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 2400 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

  • robotics
  • precision agriculture
  • digital agriculture
  • UAV
  • remote sensing

Published Papers (6 papers)

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Research

15 pages, 4976 KiB  
Article
Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm
by Yunhe Zhou, Yunchao Tang, Xiangjun Zou, Mingliang Wu, Wei Tang, Fan Meng, Yunqi Zhang and Hanwen Kang
Appl. Sci. 2022, 12(24), 12959; https://doi.org/10.3390/app122412959 - 16 Dec 2022
Cited by 42 | Viewed by 3784
Abstract
Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large [...] Read more.
Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits’ detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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16 pages, 6346 KiB  
Article
Design and Kinematic Analysis of Cable-Driven Target Spray Robot for Citrus Orchards
by Xiulan Bao, Yuxin Niu, Yishu Li, Jincheng Mao, Shanjun Li, Xiaojie Ma, Qilin Yin and Biyu Chen
Appl. Sci. 2022, 12(18), 9379; https://doi.org/10.3390/app12189379 - 19 Sep 2022
Cited by 5 | Viewed by 1577
Abstract
In Southeast Asia, many varieties of citrus are grown in hilly areas. Compared with plain orchards, it is difficult for large spraying equipment to move in hilly orchards. Small spraying equipment can enter hilly orchards, but their spraying power cannot make droplets penetrate [...] Read more.
In Southeast Asia, many varieties of citrus are grown in hilly areas. Compared with plain orchards, it is difficult for large spraying equipment to move in hilly orchards. Small spraying equipment can enter hilly orchards, but their spraying power cannot make droplets penetrate into the canopy, resulting in low deposition rates within the canopy. As a kind of unstructured narrow space, the branches within the canopy are interlaced, thus a flexible manipulator that can move within the canopy is required. In this paper, a novel remote-controlled, cable-driven target spray robot (CDTSR) was designed to achieve a precise spray within the canopy. It consisted of a small tracked vehicle, a cable-driven flexible manipulator (CDFM), and a spray system. The CDFM had six degrees of freedom driven by a cable tendon. The forward and inverse kinematics model of the CDFM were established and then the semispherical workspace was calculated. Furthermore, while considering precise control requirements, the dynamics equations were derived. The experimental results demonstrated that the CFDM could move dexterously within the canopy with interlacing branches to reach pests and diseases areas in the canopy. The entire operation took 3.5 s. This study solved the problem of a low spray deposition rate within a canopy and has potential applications in agricultural plant protection. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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13 pages, 4610 KiB  
Article
Path Tracking of a 4WIS–4WID Agricultural Machinery Based on Variable Look-Ahead Distance
by Lijun Xu, Yankun Yang, Qinhan Chen, Fengcheng Fu, Bihang Yang and Lijian Yao
Appl. Sci. 2022, 12(17), 8651; https://doi.org/10.3390/app12178651 - 29 Aug 2022
Cited by 10 | Viewed by 1547
Abstract
Aiming to solve the problem of the low path-tracking accuracy of mobile robots in agricultural environments, the authors of this paper propose a path-tracking method for agricultural machinery based on variable look-ahead distance. A kinematic model of the four wheel independent steering–four wheel [...] Read more.
Aiming to solve the problem of the low path-tracking accuracy of mobile robots in agricultural environments, the authors of this paper propose a path-tracking method for agricultural machinery based on variable look-ahead distance. A kinematic model of the four wheel independent steering–four wheel independent drive (4WIS–4WID) structure based on pure pursuit was constructed to obtain the functional equation of the current position and the four-wheel steering angle. The fuzzy controller, which takes the lateral deviation and heading deviation as input and the look-ahead distance in a pure pursuit model as output, was designed to obtain the look-ahead distance that changes dynamically with the deviation of mobile agricultural machinery. The path-tracking performance of 4WIS–4WID agricultural machinery in three scenarios (1 m, −90°; 1 m, 0°; and 0 m, 90°) with different initial deviations was tested using a pure pursuit model based on a variable look-ahead distance. The obtained field test results showed an average deviation of 19.7 cm, an average tracking time of 5.1 s, an average stability distance of 203.9 cm, and a steady state deviation of 3.1 cm. The results showed that the proposed method presents a significant path-tracking performance advantage over a fixed look-ahead distance pure tracking model and can be a reference for high-quality path-tracking methods in automatic navigation research. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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24 pages, 12466 KiB  
Article
A Novel Agricultural Machinery Intelligent Design System Based on Integrating Image Processing and Knowledge Reasoning
by Cheng’en Li, Yunchao Tang, Xiangjun Zou, Po Zhang, Junqiang Lin, Guoping Lian and Yaoqiang Pan
Appl. Sci. 2022, 12(15), 7900; https://doi.org/10.3390/app12157900 - 06 Aug 2022
Cited by 16 | Viewed by 2708
Abstract
Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machinery design systems in analyzing data, such as fit, [...] Read more.
Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machinery design systems in analyzing data, such as fit, tolerance, interchangeability, and the assembly process, as well as to overcome the disadvantages of the high cost of intelligent design modules, lack of data compatibility, and inconsistency between modules, a novel agricultural machinery intelligent design system integrating image processing and knowledge reasoning is constructed. An image-processing algorithm and trigger are used to detect the feature parameters of key parts of agricultural machinery and build a virtual prototype. At the same time, a special knowledge base of agricultural machinery is constructed to analyze the test data of the virtual prototype. The results of practical application and software evaluation of third-party institutions show that the system improves the efficiency of intelligent design in key parts of agricultural machinery by approximately 20%, reduces the operation error rate of personnel by approximately 40% and the consumption of computer resources by approximately 30%, and greatly reduces the purchase cost of intelligent design systems to provide a reference for intelligent design to guide actual production. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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15 pages, 4717 KiB  
Article
A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network
by Shaoxiong Zheng, Peng Gao, Weixing Wang and Xiangjun Zou
Appl. Sci. 2022, 12(13), 6721; https://doi.org/10.3390/app12136721 - 02 Jul 2022
Cited by 11 | Viewed by 1729
Abstract
In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained [...] Read more.
In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. The constructed 15-layer forest fire risk identification DCNN model named “DCN_Fire” could accurately identify core fire insurance areas. Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model’s accuracy. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved, and the model’s detection accuracy was further improved. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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15 pages, 766 KiB  
Article
Plant Disease Diagnosis in the Visible Spectrum
by Lili Guadarrama, Carlos Paredes and Omar Mercado
Appl. Sci. 2022, 12(4), 2199; https://doi.org/10.3390/app12042199 - 20 Feb 2022
Cited by 3 | Viewed by 1786
Abstract
A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation [...] Read more.
A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios. Full article
(This article belongs to the Special Issue Advances in Robotics and Mechatronics for Agriculture)
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