Application of Robots and Automation Technology in Agriculture

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

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 28431

Special Issue Editors


E-Mail Website
Guest Editor
1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal
2. Centre for Robotics in Industry and Intelligent Systems (CRIIS), Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal
Interests: mobile robot localization; collaborative robots; IoT; path planning; simulation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Co-Guest Editor
1. School of Science and Technology (ECT-UTAD), University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
2. Centre for Robotics in Industry and Intelligent Systems (CRIIS), Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal
Interests: modelling; control applied to industrial and agroforestry processes; automation

E-Mail Website
Co-Guest Editor
1. Habilitation at Engineering Department, UTAD—University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
Interests: educational robotics; robotic competitions; robotics for agriculture; IoT; sensors; sensors for agriculture
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Co-Guest Editor
Institute of Systems and Computer Engineering, Technology and Science (INESC TEC), 4200-465 Porto, Portugal
Interests: robotics; automation; sensor fusion; perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agricultural sector is facing several problems related to climate change, soil health depletion, water scarcity and pollution, overuse of pesticides and fertilisers, and labour costs, among others. Technological developments related to automation and robotics can support farmers in enhancing the productivity of their farms in a profitable and sustainable way. Automation, robotics, AI, and IoT enable precise farm operations and environmentally sustainable practices to be achieved by allowing precision management of nutrients and plant protection products, mechanical weed removal, harvesting, and reducing the need for human labour.

This Special Issue, “Application of Robots and Automation Technology in Agriculture”, will address new topics of robotics and automation technology applied to agriculture. The aim is to publish new works and advances in research using technological innovations to automate the processes of farming, aiding a move toward remedying the lack of human resources in the agriculture field.

Submissions are welcome from topics including—not limited to—the following:

  • Mobile robotics applied to agriculture support;
  • Unmanned aerial robotics to support agriculture tasks and monitoring of environmental applications;
  • Collaborative manipulators to support people on agricultural applications (soil preparation, seeding, crop protection);
  • Robots for pruning, thinning, harvesting, mowing, and spraying;
  • Robotic platforms for monitoring, prediction, and aiding in decision making;
  • IoT for sampling and data collection.

Prof. Dr. José Lima
Prof. Dr. José Boaventura Ribeiro da Cunha
Prof. Dr. Antonio Valente
Prof. Dr. Filipe Neves Dos Santos
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

  • robotics
  • monitoring
  • IoT
  • decision support
  • automation
  • autonomous robots

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 7665 KiB  
Article
Knowledge Discovery and Diagnosis Using Temporal-Association-Rule-Mining-Based Approach for Threshing Cylinder Blockage
by Yehong Liu, Xin Wang, Dong Dai, Can Tang, Xu Mao, Du Chen, Yawei Zhang and Shumao Wang
Agriculture 2023, 13(7), 1299; https://doi.org/10.3390/agriculture13071299 - 25 Jun 2023
Cited by 1 | Viewed by 1040
Abstract
Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to [...] Read more.
Accurately diagnosing blockages in a threshing cylinder is crucial for ensuring efficiency and quality in combine harvester operations. However, in terms of blockage diagnostic methods, the current state of affairs is characterized by model-based approaches that can be highly time-consuming and difficult to implement, while data-driven approaches lack interpretability. To address this situation, we propose a temporal association rule mining (TARM)-based fault diagnosis method for identifying threshing cylinder blockages and discovering knowledge. This study performs field trials by varying the actual feed rate and obtains datasets for three blockage classes (slight, moderate, and severe). Firstly, a symbolic aggregate approximation (SAX) method is employed to reduce the data dimensionality and to construct the transaction set with a sliding window. Next, a cSpade method is used to mine and extract strong association rules by applying improved support, confidence, and lift indicators. With the established strong association rules, this study can comprehensively elucidate the variation pattern of each characteristic under several blockage failure conditions and can effectively identify blockage faults. The results demonstrate that the proposed method effectively distinguishes between three levels of blockage faults, achieving an overall diagnostic accuracy of 0.94. And the method yields precisions of 0.90, 0.92, and 0.99 and corresponding recalls of 0.90, 0.93, and 0.98 for slight, medium, and severe levels of blockage faults, respectively. Specifically, the knowledge acquired from the extracted strong association rules can effectively explain the operational characteristics of a combine harvester when its threshing cylinders are blocked. Furthermore, the proposed approach in this study can provide a reasonable and reliable reference for future research on threshing cylinder blockages. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

22 pages, 18542 KiB  
Article
Design of a Teat Cup Attachment Robot for Automatic Milking Systems
by Chengjun Wang, Fan Ding, Liuyi Ling and Shaoqiang Li
Agriculture 2023, 13(6), 1273; https://doi.org/10.3390/agriculture13061273 - 20 Jun 2023
Cited by 1 | Viewed by 1762
Abstract
Automatic milking systems (AMSs) for medium and large dairy farms in China require manual assistance to attach the teat cup, which greatly affects the milking efficiency and labor costs. In this regard, it is necessary to realize the automatic completion of cow teat [...] Read more.
Automatic milking systems (AMSs) for medium and large dairy farms in China require manual assistance to attach the teat cup, which greatly affects the milking efficiency and labor costs. In this regard, it is necessary to realize the automatic completion of cow teat attachment work. To address this issue, the authors developed a teat cup attachment robot for an AMS based on the theory of the solution of inventive problems (TRIZ). Specifically, we developed an enhanced algorithm for teat detection and designed a six-degree-of-freedom manipulator with integrated drive control. The design parameters were simulated and analyzed to validate their efficacy, while the rationality of the manipulator’s movement during teat cup attachment was verified. The maximum displacement and angle error of the cup was 1.625 mm and 1.216 mm, respectively, as verified by the teat cup attachment error test. A dynamic response test showed that the manipulator could follow the teat of the cow in real time. The attachment time for teat cups was 21 s per cow, with a success rate of 98%. The performance of the teat cup attachment robot was capable of meeting the automatic attachment teat cup needs for medium and large dairy farms during milking. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

25 pages, 5488 KiB  
Article
Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs
by Gabriel G. R. de Castro, Guido S. Berger, Alvaro Cantieri, Marco Teixeira, José Lima, Ana I. Pereira and Milena F. Pinto
Agriculture 2023, 13(2), 354; https://doi.org/10.3390/agriculture13020354 - 31 Jan 2023
Cited by 10 | Viewed by 2755
Abstract
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given [...] Read more.
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

26 pages, 12218 KiB  
Article
Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario
by Guido S. Berger, Marco Teixeira, Alvaro Cantieri, José Lima, Ana I. Pereira, António Valente, Gabriel G. R. de Castro and Milena F. Pinto
Agriculture 2023, 13(2), 239; https://doi.org/10.3390/agriculture13020239 - 19 Jan 2023
Cited by 16 | Viewed by 2793
Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to [...] Read more.
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

22 pages, 10353 KiB  
Article
Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment
by Takuya Otani, Akira Itoh, Hideki Mizukami, Masatsugu Murakami, Shunya Yoshida, Kota Terae, Taiga Tanaka, Koki Masaya, Shuntaro Aotake, Masatoshi Funabashi and Atsuo Takanishi
Agriculture 2023, 13(1), 18; https://doi.org/10.3390/agriculture13010018 - 21 Dec 2022
Cited by 6 | Viewed by 7482
Abstract
Currently, an agricultural method called SynecocultureTM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of [...] Read more.
Currently, an agricultural method called SynecocultureTM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of plants are grown densely, and their management relies mainly on manual labor, since conventional agricultural machines and robots cannot be applied in complex vegetation. To improve work efficiency and boost regreening by scaling-up Synecoculture, we developed a robot that can sow, prune, and harvest in dense and diverse vegetation that grows under solar panels, towards the achievement of compatibility between food and energy production on a large scale. We adopted a four-wheel mechanism with sufficient ability to move on uneven terrain, and a two orthogonal axes mechanism with adjusted tool positioning while performing management tasks. In the field experiment, the robot could move straight on shelving slopes and overcome obstacles, such as small steps and weeds, and succeeded in harvesting and weeding with human operation, using the tool maneuver mechanism based on the recognition of the field situation through camera image. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

15 pages, 6824 KiB  
Article
Two-Dimensional Path Planning Platform for Autonomous Walk behind Hand Tractor
by Padma Nyoman Crisnapati and Dechrit Maneetham
Agriculture 2022, 12(12), 2051; https://doi.org/10.3390/agriculture12122051 - 29 Nov 2022
Cited by 5 | Viewed by 1976
Abstract
The use of autonomous vehicles in agriculture has increased in recent years. To fully automate agricultural missions, particularly the tillage process using the walk-behind hand tractor, the path planning problem for the robot must be solved so that all points in the intended [...] Read more.
The use of autonomous vehicles in agriculture has increased in recent years. To fully automate agricultural missions, particularly the tillage process using the walk-behind hand tractor, the path planning problem for the robot must be solved so that all points in the intended region of interest may be traced. The current planning algorithm has been successful in determining the best tillage path. On the other hand, the algorithm ignores the path built using the dynamic starting point, finish point and path distance. We propose a path planning concept for back-and-forth path patterns. Our algorithm employs a novel approach based on Laravel and Google Maps, which considers the user’s desired distance interval, start point, and finish point. We demonstrated auto-generating vertex-edge pathways in this research. Field trials using a walk-behind hand tractor in a plowing mission have been successfully conducted to validate the accuracy of the resulting waypoint coordinates. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

17 pages, 6249 KiB  
Article
Automatic Fruit Harvesting Device Based on Visual Feedback Control
by Bor-Jiunn Wen and Che-Chih Yeh
Agriculture 2022, 12(12), 2050; https://doi.org/10.3390/agriculture12122050 - 29 Nov 2022
Cited by 4 | Viewed by 2115
Abstract
With aging populations, and people′s demand for high-quality or high-unit-price fruits and vegetables, the corresponding development of automatic fruit harvesting has attracted significant attention. According to the required operating functions, based on the fruit planting environment and harvesting requirements, this study designed a [...] Read more.
With aging populations, and people′s demand for high-quality or high-unit-price fruits and vegetables, the corresponding development of automatic fruit harvesting has attracted significant attention. According to the required operating functions, based on the fruit planting environment and harvesting requirements, this study designed a harvesting mechanism to independently drive a gripper and scissor for individual tasks, which corresponded to forward or reverse rotation using a single motor. The study utilized a robotic arm in combination with the harvesting mechanism, supported by a single machine vision component, to recognize fruits by deep-learning neural networks based on a YOLOv3-tiny algorithm. The study completed the coordinate positioning of the fruit, using a two-dimensional visual sensing method (TVSM), which was used to achieve image depth measurement. Finally, impedance control, based on visual feedback from YOLOv3-tiny and the TVSM, was used to grip the fruits according to their size and rigidity, so as to avoid the fruits being gripped by excessive force; therefore, the apple harvesting task was completed with a 3.6 N contact force for an apple with a weight of 235 g and a diameter of 80 mm. During the cutting process, the contact point of the metal scissors of the motor-driven mechanism provided a shear force of 9.9 N, which was significantly smaller than the simulation result of 94 N using ADAMS and MATLAB software, even though the scissors were slightly blunt after many cuts. This study established an automatic fruit harvesting device based on visual feedback control, which can provide an automatic and convenient fruit harvest by reducing harvesting manpower. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

21 pages, 6394 KiB  
Article
Research on the End Effector and Optimal Motion Control Strategy for a Plug Seedling Transplanting Parallel Robot
by Xiong Zhao, Di Cheng, Wenxun Dong, Xingxiao Ma, Yongsen Xiong and Junhua Tong
Agriculture 2022, 12(10), 1661; https://doi.org/10.3390/agriculture12101661 - 10 Oct 2022
Cited by 6 | Viewed by 1682
Abstract
Due to the phenomenon of holes and inferior seedlings in trays, it is necessary to remove and replenish unqualified seedlings. The traditional operation is labor-intensive, and the degree of mechanization is low. This paper took broccoli seedlings as the research object and developed [...] Read more.
Due to the phenomenon of holes and inferior seedlings in trays, it is necessary to remove and replenish unqualified seedlings. The traditional operation is labor-intensive, and the degree of mechanization is low. This paper took broccoli seedlings as the research object and developed an image recognition system suitable for seedling health recognition and pose judgement, researched and designed a plug-in end effector that reduces leaf damage, and conducted orthogonal tests to obtain a substrate parameter combination containing the moisture content, seedling age, and transplanting acceleration suitable for culling operations. A parallel robot kinematics and dynamics model was built. The fifth degree B-spline curve was used to construct the joint space motion curve for seven nodes, and the motor speed, torque, and end-effector acceleration were used to construct the joint space motion curves. The end-effector acceleration was the constraint condition to plan the optimal trajectory of the joint space in time, and the optimal time was obtained using the artificial fish swarm–particle swarm hybrid optimization algorithm. A single operation time was greatly reduced; the whole machine was systematically built; the average time of single-time seedling removal was measured; and the transplanting efficiency of the whole machine was high. In the seedling damage rate gap test, the leaf damage rate was low. This research provides a reference for the localized development of greenhouse high-speed and low-loss seedling removal equipment. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

17 pages, 5097 KiB  
Article
Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester
by Weipeng Zhang, Bo Zhao, Liming Zhou, Jizhong Wang, Kang Niu, Fengzhu Wang and Ruixue Wang
Agriculture 2022, 12(6), 893; https://doi.org/10.3390/agriculture12060893 - 20 Jun 2022
Cited by 6 | Viewed by 2709
Abstract
In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this [...] Read more.
In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

21 pages, 55841 KiB  
Article
Recognition for Stems of Tomato Plants at Night Based on a Hybrid Joint Neural Network
by Rong Xiang, Maochen Zhang and Jielan Zhang
Agriculture 2022, 12(6), 743; https://doi.org/10.3390/agriculture12060743 - 24 May 2022
Cited by 6 | Viewed by 2142
Abstract
Recognition of plant stems is vital to automating multiple processes in fruit and vegetable production. The colour similarity between stems and leaves of tomato plants presents a considerable challenge for recognising stems in colour images. With duality relation in edge pairs as a [...] Read more.
Recognition of plant stems is vital to automating multiple processes in fruit and vegetable production. The colour similarity between stems and leaves of tomato plants presents a considerable challenge for recognising stems in colour images. With duality relation in edge pairs as a basis, we designed a recognition algorithm for stems of tomato plants based on a hybrid joint neural network, which was composed of the duality edge method and deep learning models. Pixel-level metrics were designed to evaluate the performance of the neural network. Tests showed that the proposed algorithm has performs well at detecting thin and long objects even if the objects have similar colour to backgrounds. Compared with other methods based on colour images, the hybrid joint neural network can recognise the main and lateral stems and has less false negatives and positives. The proposed method has low hardware cost and can be used in the automation of fruit and vegetable production, such as in automatic targeted fertilisation and spraying, deleafing, branch pruning, clustered fruit harvesting and harvesting with trunk shake, obstacle avoidance, and navigation. Full article
(This article belongs to the Special Issue Application of Robots and Automation Technology in Agriculture)
Show Figures

Figure 1

Back to TopTop