{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:45:09Z","timestamp":1780987509503,"version":"3.54.1"},"reference-count":259,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["23-0179"],"award-info":[{"award-number":["23-0179"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots\u2014including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms\u2014are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture.<\/jats:p>","DOI":"10.3390\/robotics14110159","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:38:22Z","timestamp":1761759502000},"page":"159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Comprehensive Review of Sensing, Control, and Networking in Agricultural Robots: From Perception to Coordination"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4039-0770","authenticated-orcid":false,"given":"Chijioke Leonard","family":"Nkwocha","sequence":"first","affiliation":[{"name":"Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA"},{"name":"Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5743-3549","authenticated-orcid":false,"given":"Adeayo","family":"Adewumi","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel Oluwadare","family":"Folorunsho","sequence":"additional","affiliation":[{"name":"Department of Industrial & Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Champaign, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-4316","authenticated-orcid":false,"given":"Chrisantus","family":"Eze","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0297-3725","authenticated-orcid":false,"given":"Pius","family":"Jjagwe","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3319-3862","authenticated-orcid":false,"given":"James","family":"Kemeshi","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-395X","authenticated-orcid":false,"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.24846\/v32i4y202306","article-title":"A Comprehensive Review of Applications of Robotics and Artificial Intelligence in Agricultural Operations","volume":"32","author":"Amin","year":"2023","journal-title":"Stud. Inform. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/applmech3030049","article-title":"A Review of Robots, Perception, and Tasks in Precision Agriculture","volume":"3","author":"Botta","year":"2022","journal-title":"Appl. Mech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109630","DOI":"10.1016\/j.compag.2024.109630","article-title":"A Review of the Current Status and Common Key Technologies for Agricultural Field Robots","volume":"227","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xie, D., Chen, L., Liu, L., Chen, L., and Wang, H. (2022). Actuators and Sensors for Application in Agricultural Robots: A Review. Machines, 10.","DOI":"10.3390\/machines10100913"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"110161","DOI":"10.1016\/j.compag.2025.110161","article-title":"Reconfigurable Agricultural Robotics: Control Strategies, Communication, and Applications","volume":"234","author":"Pedraza","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Peng, Y., Liu, J., Xie, B., Shan, H., He, M., Hou, G., and Jin, Y. (2021, January 27\u201331). Research Progress of Urban Dual-Arm Humanoid Grape Harvesting Robot. Proceedings of the 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Jiaxing, China.","DOI":"10.1109\/CYBER53097.2021.9588266"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, S., Wang, S., Yi, Z., Zhang, M., and Lv, X. (2022). Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.815218"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11712","DOI":"10.1109\/JSEN.2020.3016081","article-title":"Augmented Perception for Agricultural Robots Navigation","volume":"21","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10462-024-11100-x","article-title":"Deep Learning and Computer Vision in Plant Disease Detection: A Comprehensive Review of Techniques, Models, and Trends in Precision Agriculture","volume":"58","author":"Upadhyay","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nkwocha, C.L., and Chandel, A.K. (2025). Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects. Computers, 14.","DOI":"10.3390\/computers14100443"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.1007\/s00521-023-08289-3","article-title":"EOS-3D-DCNN: Ebola Optimization Search-Based 3D-Dense Convolutional Neural Network for Corn Leaf Disease Prediction","volume":"35","author":"Ashwini","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Singla, A., Nehra, A., Joshi, K., Kumar, A., Tuteja, N., Varshney, R.K., Gill, S.S., and Gill, R. (2024). Exploration of Machine Learning Approaches for Automated Crop Disease Detection. Curr. Plant Biol., 40.","DOI":"10.1016\/j.cpb.2024.100382"},{"key":"ref_13","first-page":"100583","article-title":"UAV-Thermal Imaging: A Technological Breakthrough for Monitoring and Quantifying Crop Abiotic Stress to Help Sustain Productivity on Sodic Soils\u2014A Case Review on Wheat","volume":"23","author":"Das","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Singh, A.P., Yerudkar, A., Mariani, V., Iannelli, L., and Glielmo, L. (2022). A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications. Remote Sens., 14.","DOI":"10.3390\/rs14071604"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Madro\u00f1al, D., Palumbo, F., Capotondi, A., and Marongiu, A. (2021, January 18\u201320). Unmanned Vehicles in Smart Farming: A Survey and a Glance at Future Horizons. Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools Proceedings, Budapest, Hungary.","DOI":"10.1145\/3444950.3444958"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Tian, T., and Yin, J. (2021). A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China. Remote Sens., 13.","DOI":"10.3390\/rs13061221"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1755-1315\/275\/1\/012022","article-title":"A Review on the Use of Drones for Precision Agriculture","volume":"275","author":"Daponte","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_18","first-page":"102590","article-title":"Deep Color Calibration for UAV Imagery in Crop Monitoring Using Semantic Style Transfer with Local to Global Attention","volume":"104","author":"Huang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Negash, L., Kim, H.-Y., and Choi, H.-L. (2019, January 1\u20133). Emerging UAV Applications in Agriculture. Proceedings of the 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), Daejeon, Republic of Korea.","DOI":"10.1109\/RITAPP.2019.8932853"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1080\/00380768.2020.1738899","article-title":"Satellite- and Drone-Based Remote Sensing of Crops and Soils for Smart Farming\u2014A Review","volume":"66","author":"Inoue","year":"2020","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Panday, U.S., Pratihast, A.K., Aryal, J., and Kayastha, R.B. (2020). A Review on Drone-Based Data Solutions for Cereal Crops. Drones, 4.","DOI":"10.3390\/drones4030041"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rahman, M.F.F., Fan, S., Zhang, Y., and Chen, L. (2021). A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture. Agriculture, 11.","DOI":"10.3390\/agriculture11010022"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Spoorthi, S., Shadaksharappa, B., Suraj, S., and Manasa, V.K. (2017, January 23\u201324). Freyr Drone: Pesticide\/Fertilizers Spraying Drone\u2014An Agricultural Approach. Proceedings of the 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India.","DOI":"10.1109\/ICCCT2.2017.7972289"},{"key":"ref_24","first-page":"22","article-title":"Performances Evaluation of Four Typical Unmanned Aerial Vehicles Used for Pesticide Application in China","volume":"10","author":"Shilin","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Guisuraga, J.M., Sanz-Ablanedo, E., Su\u00e1rez-Seoane, S., and Calvo, L. (2018). Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges. Sensors, 18.","DOI":"10.3390\/s18020586"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100940","DOI":"10.1016\/j.atech.2025.100940","article-title":"Numerical Simulation of the Effects of Downwash Airflow and Crosswinds on the Spray Performance of Quad-Rotor Agricultural UAVs","volume":"11","author":"Guo","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2025.05.009","article-title":"A Novel Real-Time Matching and Pose Reconstruction Method for Low-Overlap Agricultural UAV Images with Repetitive Textures","volume":"226","author":"Xiao","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","first-page":"101131","article-title":"Yield Prediction Models of Organic Oil Rose Farming with Agricultural Unmanned Aerial Vehicles (UAVs) Images and Machine Learnaing Algorithms","volume":"33","author":"Demir","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107912","DOI":"10.1016\/j.compeleceng.2022.107912","article-title":"An Intelligent WSN-UAV-Based IoT Framework for Precision Agriculture Application","volume":"100","author":"Singh","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Park, M., Lee, S., and Lee, S. (2020). Dynamic Topology Reconstruction Protocol for UAV Swarm Networking. Symmetry, 12.","DOI":"10.3390\/sym12071111"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Zhu, L. (2023). A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones, 7.","DOI":"10.3390\/drones7060398"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7963","DOI":"10.1007\/s13369-022-06738-0","article-title":"Recent Advances in Unmanned Aerial Vehicles: A Review","volume":"47","author":"Ahmed","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s41315-024-00359-6","article-title":"Review on Design, Development, and Implementation of an Unmanned Aerial Vehicle for Various Applications","volume":"9","author":"Shekh","year":"2025","journal-title":"Int. J. Intell. Robot. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, X., Shao, Q., Li, Y., Wang, Y., Wang, D., Liu, J., Fan, J., and Yang, F. (2018). Application of UAV Remote Sensing for a Population Census of Large Wild Herbivores\u2014Taking the Headwater Region of the Yellow River as an Example. Remote Sens., 10.","DOI":"10.3390\/rs10071041"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/LCSYS.2018.2845546","article-title":"Sample-Based SMPC for Tracking Control of Fixed-Wing UAV","volume":"2","author":"Mammarella","year":"2018","journal-title":"IEEE Control Syst. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pfeifer, C., Barbosa, A., Mustafa, O., Peter, H.-U., R\u00fcmmler, M.-C., and Brenning, A. (2019). Using Fixed-Wing UAV for Detecting and Mapping the Distribution and Abundance of Penguins on the South Shetlands Islands, Antarctica. Drones, 3.","DOI":"10.3390\/drones3020039"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110083","DOI":"10.1016\/j.ast.2025.110083","article-title":"Experimental and Numerical Investigation on the Spraying Performance of an Agricultural Unmanned Aerial Vehicle","volume":"160","author":"Divazi","year":"2025","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"012026","DOI":"10.1088\/1755-1315\/1284\/1\/012026","article-title":"Productivity Analysis of Agricultural UAVs by Field Crop Spraying","volume":"1284","author":"Kovalev","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ukaegbu, U.F., Tartibu, L.K., Okwu, M.O., and Olayode, I.O. (2021). Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture. Sensors, 21.","DOI":"10.3390\/s21134417"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"032002","DOI":"10.1088\/1755-1315\/548\/3\/032002","article-title":"Digitization of Agricultural Land Using an Unmanned Aerial Vehicle","volume":"548","author":"Abramov","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, P.-C., Chiang, Y.-C., and Weng, P.-Y. (2020). Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification. Agriculture, 10.","DOI":"10.3390\/agriculture10090416"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105817","DOI":"10.1016\/j.compag.2020.105817","article-title":"Adaptive Autonomous UAV Scouting for Rice Lodging Assessment Using Edge Computing with Deep Learning EDANet","volume":"179","author":"Yang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ju, C., and Son, H.I. (2018). Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation. Electronics, 7.","DOI":"10.3390\/electronics7090162"},{"key":"ref_44","first-page":"30","article-title":"Use of Unmanned Aerial Vehicles for Agricultural Applications with Emphasis on Crop Protection: Three Novel Case-Studies","volume":"5","author":"Psirofonia","year":"2017","journal-title":"Int. J. Agric. Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., Serrano, N., Arquero, O., and Pe\u00f1a, J.M. (2015). High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130479"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.01.008","article-title":"Multi-Temporal Imaging Using an Unmanned Aerial Vehicle for Monitoring a Sunflower Crop","volume":"132","author":"Vega","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khan, N., Ray, R.L., Sargani, G.R., Ihtisham, M., Khayyam, M., and Ismail, S. (2021). Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture. Sustainability, 13.","DOI":"10.3390\/su13094883"},{"key":"ref_48","first-page":"1474","article-title":"Product Lifecycle Management in Knowledge Intensive Collaborative Environments: An Application to Automotive Industry","volume":"37","author":"Ferreira","year":"2017","journal-title":"Int. J. Inf. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Singh, S., Vaishnav, R., Gautam, S., and Banerjee, S. (2024, January 15\u201316). Agricultural Robotics: A Comprehensive Review of Applications, Challenges and Future Prospects. Proceedings of the 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), Namakkal, India.","DOI":"10.1109\/AIMLA59606.2024.10531517"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110023","DOI":"10.1016\/j.compag.2025.110023","article-title":"Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms","volume":"232","author":"Teng","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s44279-024-00113-3","article-title":"A Ground Robotic System for Crops and Soil Monitoring and Data Collection in New Mexico Chile Pepper Farms","volume":"2","author":"Linford","year":"2024","journal-title":"Discov. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103906","DOI":"10.1016\/j.robot.2021.103906","article-title":"Design, Development and Evaluation of Latex Harvesting Robot Based on Flexible Toggle","volume":"147","author":"Wang","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hemanth Kumar, N., Suresh, R., and Balappa, B.U. (2023, January 14\u201316). Development of an Unmanned Ground Vehicle for Pesticide Spraying in Chilli Crop. Proceedings of the 2023 IEEE Technology & Engineering Management Conference-Asia Pacific (TEMSCON-ASPAC), Bengaluru, India.","DOI":"10.1109\/TEMSCON-ASPAC59527.2023.10531348"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"9760269","DOI":"10.34133\/2022\/9760269","article-title":"A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots","volume":"2022","author":"Xu","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_55","first-page":"e20207761","article-title":"Agricultural Unmanned Ground Vehicles: A Review from the Stability Point of View","volume":"51","author":"Fernandes","year":"2021","journal-title":"Rev. Ci\u00eanc. Agron\u00f4mica"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"98660Q","DOI":"10.1117\/12.2224248","article-title":"A Survey of Unmanned Ground Vehicles with Applications to Agricultural and Environmental Sensing","volume":"Volume 9866","author":"Valasek","year":"2016","journal-title":"Proceedings of the SPIE Proceedings"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jterra.2020.06.006","article-title":"A Review of Autonomous Agricultural Vehicles (The Experience of Hokkaido University)","volume":"91","author":"Roshanianfard","year":"2020","journal-title":"J. Terramechanics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Etezadi, H., and Eshkabilov, S. (2024). A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture. Agriculture, 14.","DOI":"10.3390\/agriculture14020163"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Pham, V., Malladi, B., Moreno, F., Gonzalez, C., Bhandari, S., and Raheja, A. (2025). Collaboration between Aerial and Ground Robots for Weed Detection and Removal. Precision Agriculture\u2019 25, Wageningen Academic.","DOI":"10.1163\/9789004725232_126"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/rob.22422","article-title":"3D Hybrid Path Planning for Optimized Coverage of Agricultural Fields: A Novel Approach for Wheeled Robots","volume":"42","author":"Spisser","year":"2025","journal-title":"J. Field Robot."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"110069","DOI":"10.1016\/j.compag.2025.110069","article-title":"A Composite Sliding Mode Controller with Extended Disturbance Observer for 4WSS Agricultural Robots in Unstructured Farmlands","volume":"232","author":"Zhang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","first-page":"610","article-title":"An Autonomous Navigation Method for Field Phenotyping Robot Based on Ground-Air Collaboration","volume":"15","author":"Zhang","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"ref_63","unstructured":"Bani\u0107, M., Stojanovi\u0107, L., Peri\u0107, M., Rangelov, D., Pavlovi\u0107, V., Miltenovi\u0107, A., and Simonovi\u0107, M. (2025, January 19\u201321). AgAR: A Multipurpose Robotic Platform for the Digital Transformation of Agriculture. Proceedings of the 11th International Scientific Conference IRMES 2025, Vrnja\u010dka Banja, Serbia."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Dokic, K., Kukina, H., and Mikolcevic, H. (2024, January 14\u201315). A Low-Cost Agriculture Robot for Dataset Creation-Software and Hardware Solutions. Proceedings of the 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR), Muscat, Oman.","DOI":"10.1109\/ICIESTR60916.2024.10798337"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1002\/rob.22238","article-title":"Deep Learning-Based Crop Row Detection for Infield Navigation of Agri-Robots","volume":"41","author":"Cielniak","year":"2024","journal-title":"J. Field Robot."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"107410","DOI":"10.1016\/j.compag.2022.107410","article-title":"Navigation and Control Development for a Four-Wheel-Steered Mobile Orchard Robot Using Model-Based Design","volume":"202","author":"Raikwar","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ejcon.2021.02.005","article-title":"Intelligent Coordinated Control of an Autonomous Tractor-Trailer and a Combine Harvester","volume":"59","author":"Shojaei","year":"2021","journal-title":"Eur. J. Control"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Skocze\u0144, M., Ochman, M., Spyra, K., Nikodem, M., Krata, D., Panek, M., and Paw\u0142owski, A. (2021). Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras. Sensors, 21.","DOI":"10.3390\/s21165292"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"106301","DOI":"10.1016\/j.compag.2021.106301","article-title":"Using a Depth Camera for Crop Row Detection and Mapping for Under-Canopy Navigation of Agricultural Robotic Vehicle","volume":"188","author":"Gai","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Mammarella, M., Comba, L., Biglia, A., Dabbene, F., and Gay, P. (2020, January 4\u20136). Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles. Proceedings of the 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento, Italy.","DOI":"10.1109\/MetroAgriFor50201.2020.9277573"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compag.2018.08.043","article-title":"Robotic In-Row Weed Control in Vegetables","volume":"154","author":"Utstumo","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mueller-Sim, T., Jenkins, M., Abel, J., and Kantor, G. (June, January 29). The Robotanist: A Ground-Based Agricultural Robot for High-Throughput Crop Phenotyping. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989418"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1007\/s11119-016-9476-3","article-title":"Fleets of Robots for Environmentally-Safe Pest Control in Agriculture","volume":"18","author":"Ribeiro","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TMECH.2015.2492984","article-title":"Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles","volume":"21","author":"Kayacan","year":"2016","journal-title":"IEEEASME Trans. Mechatron."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1002\/rob.22178","article-title":"Development, Integration, and Field Evaluation of an Autonomous Citrus-harvesting Robot","volume":"40","author":"Yin","year":"2023","journal-title":"J. Field Robot."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Davidson, J.R., and Mo, C. (2015, January 13\u201319). Mechanical Design and Initial Performance Testing of an Apple-Picking End-Effector. Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition, Houston, TX, USA. Volume 4A: Dynamics, Vibration, and Control.","DOI":"10.1115\/IMECE2015-50482"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.3390\/agriengineering5040136","article-title":"Development Challenges of Fruit-Harvesting Robotic Arms: A Critical Review","volume":"5","author":"Kaleem","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s42835-023-01596-8","article-title":"Review of Research Advances in Fruit and Vegetable Harvesting Robots","volume":"19","author":"Xiao","year":"2024","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_79","first-page":"33","article-title":"Review of Smart Robots for Fruit and Vegetable Picking in Agriculture","volume":"15","author":"Wang","year":"2022","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"106879","DOI":"10.1016\/j.compag.2022.106879","article-title":"Development and Evaluation of a Pneumatic Finger-like End-Effector for Cherry Tomato Harvesting Robot in Greenhouse","volume":"197","author":"Gao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2024.01.009","article-title":"Picking Patterns Evaluation for Cherry Tomato Robotic Harvesting End-Effector Design","volume":"239","author":"Gao","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Yeshmukhametov, A., Koganezawa, K., Yamamoto, Y., Buribayev, Z., Mukhtar, Z., and Amirgaliyev, Y. (2022). Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes. Appl. Sci., 12.","DOI":"10.3390\/app12146922"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"012060","DOI":"10.1088\/1742-6596\/2246\/1\/012060","article-title":"Design of Flexible Spherical Fruit and Vegetable Picking End-Effector Based on Vision Recognition","volume":"2246","author":"Chen","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"108023","DOI":"10.1016\/j.compag.2023.108023","article-title":"Robotic Tree-Fruit Harvesting with Arrays of Cartesian Arms: A Study of Fruit Pick Cycle Times","volume":"211","author":"Arikapudi","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"108022","DOI":"10.1016\/j.compag.2023.108022","article-title":"Citrus Pose Estimation from an RGB Image for Automated Harvesting","volume":"211","author":"Sun","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"107026","DOI":"10.1016\/j.compag.2022.107026","article-title":"Contact Force Modeling and Variable Damping Impedance Control of Apple Harvesting Robot","volume":"198","author":"Ji","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Xiao, X., Wang, Y., and Jiang, Y. (2022). End-Effectors Developed for Citrus and Other Spherical Crops. Appl. Sci., 12.","DOI":"10.3390\/app12157945"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"106353","DOI":"10.1016\/j.compag.2021.106353","article-title":"Three-Finger Grasp Planning and Experimental Analysis of Picking Patterns for Robotic Apple Harvesting","volume":"188","author":"Fan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_89","unstructured":"Roshanianfard, A. (2018). Development of a Harvesting Robot for Heavy-Weight Crop. [Doctoral Dissertation, Hokkaido University]."},{"key":"ref_90","first-page":"172","article-title":"Design of a 4 DOF Parallel Robot Arm and the Firmware Implementation on Embedded System to Transplant Pot Seedlings","volume":"4","author":"Rahul","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.compag.2019.01.009","article-title":"Development and Field Evaluation of a Strawberry Harvesting Robot with a Cable-Driven Gripper","volume":"157","author":"Xiong","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"106145","DOI":"10.1016\/j.compag.2021.106145","article-title":"Picking Dynamic Analysis for Robotic Harvesting of Agaricus Bisporus Mushrooms","volume":"185","author":"Huang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_93","first-page":"14","article-title":"Development of a Robot for Harvesting Strawberries","volume":"51","author":"Anthonis","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_94","first-page":"155","article-title":"Development of a 5DOF Robotic Arm (RAVebots-1) Applied to Heavy Products Harvesting","volume":"49","author":"Roshanianfard","year":"2016","journal-title":"IFAC-Pap."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.13031\/trans.14295","article-title":"Development of a Small Electric Robot Boat for Mowing Aquatic Weeds","volume":"64","author":"Kaizu","year":"2021","journal-title":"Trans. ASABE"},{"key":"ref_96","first-page":"101","article-title":"Development of an Automatic Operation Control System for a Weeding Robot in Paddy Fields to Track a Target Path and Speed","volume":"16","author":"Moro","year":"2023","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.matpr.2022.06.118","article-title":"Implementation of In-Row Weeding Robot with Novel Wheel, Assembly and Wheel Angle Adjustment for Slurry Paddy Field","volume":"65","author":"Murugaraj","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2019.04.009","article-title":"Development of a Positioning System Using UAV-Based Computer Vision for an Airboat Navigation in Paddy Field","volume":"162","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_99","first-page":"21","article-title":"Development of an Unmanned Surface Vehicle for Autonomous Navigation in a Paddy Field","volume":"9","author":"Liu","year":"2016","journal-title":"Eng. Agric. Environ. Food"},{"key":"ref_100","first-page":"88","article-title":"Simulation and Test of an Agricultural Unmanned Airboat Maneuverability Model","volume":"10","author":"Liu","year":"2017","journal-title":"Biol Eng"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.biosystemseng.2016.06.014","article-title":"Agricultural Robots for Field Operations: Concepts and Components","volume":"149","author":"Bechar","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Qu, J., Zhang, Z., Qin, Z., Guo, K., and Li, D. (2024). Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review. Machines, 12.","DOI":"10.20944\/preprints202402.0401.v1"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Huang, Y., Fu, J., Xu, S., Han, T., and Liu, Y. (2022). Research on Integrated Navigation System of Agricultural Machinery Based on RTK-BDS\/INS. Agriculture, 12.","DOI":"10.3390\/agriculture12081169"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Sun, T., Le, F., Cai, C., Jin, Y., Xue, X., and Cui, L. (2025). Soybean\u2013Corn Seedling Crop Row Detection for Agricultural Autonomous Navigation Based on GD-YOLOv10n-Seg. Agriculture, 15.","DOI":"10.3390\/agriculture15070796"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1109\/LRA.2025.3541335","article-title":"P-AgNav: Range View-Based Autonomous Navigation System for Cornfields","volume":"10","author":"Kim","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Mansur, H., Gadhwal, M., Abon, J.E., and Flippo, D. (2025). Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV. Agriculture, 15.","DOI":"10.20944\/preprints202503.0244.v1"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"116970","DOI":"10.1016\/j.image.2023.116970","article-title":"SLDF: A Semantic Line Detection Framework for Robot Guidance","volume":"115","author":"Chen","year":"2023","journal-title":"Signal Process. Image Commun."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"107811","DOI":"10.1016\/j.compag.2023.107811","article-title":"Study of Convolutional Neural Network-Based Semantic Segmentation Methods on Edge Intelligence Devices for Field Agricultural Robot Navigation Line Extraction","volume":"209","author":"Yu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"105911","DOI":"10.1016\/j.compag.2020.105911","article-title":"Navigation Path Extraction for Greenhouse Cucumber-Picking Robots Using the Prediction-Point Hough Transform","volume":"180","author":"Chen","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s44163-025-00301-0","article-title":"Deep Learning-Based Semantic Segmentation with Novel Navigation Line Extraction for Autonomous Agricultural Robots","volume":"5","author":"Nkwocha","year":"2025","journal-title":"Discov. Artif. Intell."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2016.06.022","article-title":"A Review of Key Techniques of Vision-Based Control for Harvesting Robot","volume":"127","author":"Zhao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_112","first-page":"140","article-title":"Development of a Tomato Harvesting Robot Used in Greenhouse","volume":"10","author":"Lili","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","article-title":"Sensors and Systems for Fruit Detection and Localization: A Review","volume":"116","author":"Gongal","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1111\/ijfs.16862","article-title":"Principle and Applications of Near-Infrared Imaging for Fruit Quality Assessment\u2014An Overview","volume":"59","author":"Patel","year":"2024","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.compag.2020.105475","article-title":"Using Color and 3D Geometry Features to Segment Fruit Point Cloud and Improve Fruit Recognition Accuracy","volume":"174","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/LRA.2017.2651952","article-title":"Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting\u2014Combined Color and 3-D Information","volume":"2","author":"Sa","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-019-09654-w","article-title":"Color-, Depth-, and Shape-Based 3D Fruit Detection","volume":"21","author":"Lin","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.compind.2018.03.017","article-title":"A Vision Methodology for Harvesting Robot to Detect Cutting Points on Peduncles of Double Overlapping Grape Clusters in a Vineyard","volume":"99","author":"Luo","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"M\u00e9ndez, V., Velasco, J., Rodr\u00edguez, F., Berenguel, M., Mart\u00ednez, A., and Guzm\u00e1n, J.L. (2019). In-Field Estimation of Orange Number and Size by 3D Laser Scanning. Agronomy, 9.","DOI":"10.3390\/agronomy9120885"},{"key":"ref_120","first-page":"45","article-title":"Kiwifruit Detection in Field Images Using Faster R-CNN with ZFNet","volume":"51","author":"Fu","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural Object Detection with You Only Look Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40648-019-0141-2","article-title":"An Automated Fruit Harvesting Robot by Using Deep Learning","volume":"6","author":"Onishi","year":"2019","journal-title":"ROBOMECH J."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1002\/rob.21888","article-title":"A Field-Tested Robotic Harvesting System for Iceberg Lettuce","volume":"37","author":"Birrell","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep Learning\u2013Method Overview and Review of Use for Fruit Detection and Yield Estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s11119-019-09662-w","article-title":"Fruit Detection in Natural Environment Using Partial Shape Matching and Probabilistic Hough Transform","volume":"21","author":"Lin","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit Detection for Strawberry Harvesting Robot in Non-Structural Environment Based on Mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"105469","DOI":"10.1016\/j.compag.2020.105469","article-title":"Integrated Detection of Citrus Fruits and Branches Using a Convolutional Neural Network","volume":"174","author":"Yang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Wang, W., Lin, C., Shui, H., Zhang, K., and Zhai, R. (2025). Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments. Plants, 14.","DOI":"10.3390\/plants14132080"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Zhao, H., Tang, Z., Li, Z., Dong, Y., Si, Y., Lu, M., and Panoutsos, G. (2024, January 25\u201327). Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach. Proceedings of the 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, UK.","DOI":"10.1109\/ICIT58233.2024.10540740"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Hu, N., Su, D., Wang, S., Nyamsuren, P., Qiao, Y., Jiang, Y., and Cai, Y. (2022). LettuceTrack: Detection and Tracking of Lettuce for Robotic Precision Spray in Agriculture. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1003243"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.biosystemseng.2015.12.010","article-title":"Ambient Awareness for Agricultural Robotic Vehicles","volume":"146","author":"Reina","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Yan, J., and Liu, Y. (2018, January 12\u201315). A Stereo Visual Obstacle Detection Approach Using Fuzzy Logic and Neural Network in Agriculture. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8665288"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Zhao, N., Zhou, L., Wang, M., Yang, L., Fang, H., He, Y., and Liu, Y. (2020). Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT. Sensors, 20.","DOI":"10.3390\/s20154082"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"106104","DOI":"10.1016\/j.compag.2021.106104","article-title":"Dynamic Obstacle Detection Based on Panoramic Vision in the Moving State of Agricultural Machineries","volume":"184","author":"Xu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compag.2015.05.015","article-title":"ilhan Localization and Control of an Autonomous Orchard Vehicle","volume":"115","author":"Bayar","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1002\/rob.21644","article-title":"Vision-based Obstacle Detection and Navigation for an Agricultural Robot","volume":"33","author":"Ball","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s10514-020-09924-x","article-title":"A Velocity Control Strategy for Collision Avoidance of Autonomous Agricultural Vehicles","volume":"44","author":"Xue","year":"2020","journal-title":"Auton. Robots"},{"key":"ref_138","first-page":"673","article-title":"Research on Static Path Planning Method of Small Obstacles for Automatic Navigation of Agricultural Machinery","volume":"51","author":"Liu","year":"2018","journal-title":"IFAC-Pap."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Liu, Z., L\u00fc, Z., Zheng, W., Zhang, W., and Cheng, X. (2019). Design of Obstacle Avoidance Controller for Agricultural Tractor Based on ROS. Int. J. Agric. Biol. Eng., 12.","DOI":"10.25165\/j.ijabe.20191206.4907"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Chen, H., Xie, H., Sun, L., and Shang, T. (2023). Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste. Agriculture, 13.","DOI":"10.3390\/agriculture13050934"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1049\/joe.2019.0840","article-title":"Path Planning of Autonomous Agricultural Machineries in Complex Rural Road","volume":"2020","author":"Cui","year":"2020","journal-title":"J. Eng."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"25005","DOI":"10.1109\/ACCESS.2022.3153496","article-title":"Collision Avoidance Considering Iterative B\u00e9zier Based Approach for Steep Slope Terrains","volume":"10","author":"Santos","year":"2022","journal-title":"IEEE Access"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Wang, Y.J., Pan, G.T., Xue, C.L., and Yang, F.Z. (2019, January 26\u201327). Research on Model of Laser Navigation System and Obstacle Avoidance for Orchard Unmanned Vehicle. Proceedings of the 2019 2nd International Conference on Informatics, Control and Automation, Hangzhou, China.","DOI":"10.1109\/ICCAR.2019.8813356"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Yang, J., Ni, J., Li, Y., Wen, J., and Chen, D. (2022). The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning. Sensors, 22.","DOI":"10.3390\/s22124316"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Bansal, A., Sikka, K., Sharma, G., Chellappa, R., and Divakaran, A. (2018, January 8\u201314). Zero-Shot Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_24"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/TCSVT.2019.2899569","article-title":"Zero Shot Detection","volume":"30","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"94370","DOI":"10.1109\/ACCESS.2025.3573324","article-title":"Automatic Navigation and Self-Driving Technology in Agricultural Machinery: A State-of-the-Art Systematic Review","volume":"13","author":"Hossain","year":"2025","journal-title":"IEEE Access"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"108517","DOI":"10.1016\/j.compag.2023.108517","article-title":"Synthetic Data Augmentation by Diffusion Probabilistic Models to Enhance Weed Recognition","volume":"216","author":"Chen","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"De Clercq, D., Nehring, E., Mayne, H., and Mahdi, A. (2024). Large Language Models Can Help Boost Food Production, but Be Mindful of Their Risks. Front. Artif. Intell., 7.","DOI":"10.3389\/frai.2024.1326153"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Sun, L., Jha, D.K., Hori, C., Jain, S., Corcodel, R., Zhu, X., Tomizuka, M., and Romeres, D. (2024, January 13\u201317). Interactive Planning Using Large Language Models for Partially Observable Robotic Tasks. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610981"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Hori, C., Kambara, M., Sugiura, K., Ota, K., Khurana, S., Jain, S., Corcodel, R., Jha, D.K., Romeres, D., and Le Roux, J. (2025, January 6\u201311). Interactive Robot Action Replanning Using Multimodal LLM Trained from Human Demonstration Videos. Proceedings of the ICASSP 2025\u20142025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India.","DOI":"10.1109\/ICASSP49660.2025.10887717"},{"key":"ref_152","unstructured":"Li, P., An, Z., Abrar, S., and Zhou, L. (2025). Large Language Models for Multi-Robot Systems: A Survey. arXiv."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Zhu, H., Qin, S., Su, M., Lin, C., Li, A., and Gao, J. (2025). Harnessing Large Vision and Language Models in Agriculture: A Review. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1579355"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Valavanis, K.P., and Vachtsevanos, G.J. (2015). Linear Flight Control Techniques for Unmanned Aerial Vehicles. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-90-481-9707-1"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"109631","DOI":"10.1016\/j.compag.2024.109631","article-title":"Integrating UAV, UGV and UAV-UGV Collaboration in Future Industrialized Agriculture: Analysis, Opportunities and Challenges","volume":"227","author":"Ren","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1002\/agj2.21346","article-title":"Precision Livestock Farming Applied to Grazingland Monitoring and Management\u2014A Review","volume":"116","author":"Bretas","year":"2024","journal-title":"Agron. J."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1109\/ACCESS.2019.2932119","article-title":"Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Yu, S., Zhu, J., Zhou, J., Cheng, J., Bian, X., Shen, J., and Wang, P. (2022). Key Technology Progress of Plant-Protection UAVs Applied to Mountain Orchards: A Review. Agronomy, 12.","DOI":"10.3390\/agronomy12112828"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Li, P., Liu, D., and Baldi, S. (2021, January 13\u201316). Plug-and-Play Adaptation in Autopilot Architectures for Unmanned Aerial Vehicles. Proceedings of the IECON 2021\u201447th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada.","DOI":"10.1109\/IECON48115.2021.9589106"},{"key":"ref_160","unstructured":"Ulus, S., and Ikbal, E. (2018, January 2\u20136). Lateral and Longitudinal Dynamics Control of a Fixed Wing UAV by Using PID Controller. Proceedings of the 4th International Conference on Engineering and Natural Sciences, Kiev, Ukraine."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Wei, X., XianYu, W., Jiazhen, L., and Yasheng, Y. (2023). Design of Anti-Load Perturbation Flight Trajectory Stability Controller for Agricultural UAV. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1030203"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Surur, K., Kabir, I., Ahmad, G., and Abido, M.A. (2025). Optimal Gain Scheduling for Fault-Tolerant Control of Quadrotor UAV Using Genetic Algorithm-Based Neural Network. Arab. J. Sci. Eng.","DOI":"10.1007\/s13369-025-09994-y"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Wu, H., Liu, D., Zhao, Y., Liu, Z., Liang, Y., Liu, Z., Huang, T., Liang, K., Xie, S., and Li, J. (2024). Establishment and Verification of the UAV Coupled Rotor Airflow Backward Tilt Angle Controller. Drones, 8.","DOI":"10.3390\/drones8040146"},{"key":"ref_164","first-page":"266","article-title":"Embedded Model Control for UAV Quadrotor via Feedback Linearization","volume":"49","author":"Lotufo","year":"2016","journal-title":"IFAC-Pap."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Shen, Z., and Tsuchiya, T. (2022). Singular Zone in Quadrotor Yaw\u2013Position Feedback Linearization. Drones, 6.","DOI":"10.3390\/drones6040084"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Lee, S., Cho, H., Yoon, K.-J., and Lee, J. (2013). Backstepping Control of Quadrotor-Type UAVs and Its Application to Teleoperation over the Internet. Intelligent Autonomous Systems 12: Volume 2 Proceedings of the 12th International Conference IAS-12, Jeju Island, Republic of Korea, 26\u201329 June 2012, Springer.","DOI":"10.1007\/978-3-642-33932-5"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Saibi, A., Boushaki, R., and Belaidi, H. (2022). Backstepping Control of Drone. Eng. Proc., 14.","DOI":"10.3390\/engproc2022014004"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"2834","DOI":"10.1002\/asjc.2722","article-title":"A Two-Loop Group Formation Tracking Control Scheme for Networked Tri-Rotor UAVs Using an ARE-Based Approach","volume":"24","author":"Bhowmick","year":"2022","journal-title":"Asian J. Control"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"115380","DOI":"10.1016\/j.eswa.2021.115380","article-title":"Intelligent Control of an UAV with a Cable-Suspended Load Using a Neural Network Estimator","volume":"183","author":"Santos","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1177\/00202940241252724","article-title":"Tracking Controller Design for Quadrotor UAVs under External Disturbances Using a High-Order Sliding Mode-Assisted Disturbance Observer","volume":"58","author":"Sun","year":"2025","journal-title":"Meas. Control"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"66607","DOI":"10.1109\/ACCESS.2025.3558615","article-title":"Advanced Optimization Methods for Nonlinear Backstepping Controllers for Quadrotor-Slung Load Systems","volume":"13","author":"Maaruf","year":"2025","journal-title":"IEEE Access"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"109676","DOI":"10.1016\/j.ast.2024.109676","article-title":"Robust Adaptive Control Law Design for Enhanced Stability of Agriculture UAV Used for Pesticide Spraying","volume":"155","author":"Ijaz","year":"2024","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"e20098","DOI":"10.1002\/ppj2.20098","article-title":"Adoption of Unoccupied Aerial Systems in Agricultural Research","volume":"7","author":"Lachowiec","year":"2024","journal-title":"Plant Phenome J."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Wen, S., Zhang, Q., Deng, J., Lan, Y., Yin, X., and Shan, J. (2018). Design and Experiment of a Variable Spray System for Unmanned Aerial Vehicles Based on PID and PWM Control. Appl. Sci., 8.","DOI":"10.3390\/app8122482"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Sharma, R., Kannojiya, R., Garg, N., and Gautam, S.S. (2023). Farming System: Quadcopter Fabrication and Development. Advances in Engineering Design, Springer Nature.","DOI":"10.1007\/978-981-99-3033-3"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10846-020-01265-2","article-title":"Feedback Linearization with Zero Dynamics Stabilization for Quadrotor Control","volume":"101","author":"Martins","year":"2020","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Villa, D.K.D., Brand\u00e3o, A.S., and Sarcinelli-Filho, M. (2019, January 2\u20136). Path-Following and Attitude Control of a Payload Using Multiple Quadrotors. Proceedings of the 2019 19th International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil.","DOI":"10.1109\/ICAR46387.2019.8981559"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10015-020-00655-x","article-title":"Feedback Linearization Control for a Tandem Rotor UAV Robot Equipped with Two 2-DOF Tiltable Coaxial-Rotors","volume":"26","author":"Xu","year":"2021","journal-title":"Artif. Life Robot."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"7861","DOI":"10.1109\/LRA.2021.3101878","article-title":"Image-Based Visual Servoing of Rotorcrafts to Planar Visual Targets of Arbitrary Orientation","volume":"6","author":"Li","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Shi, Y., Ijaz, S., He, Z., Xu, Z., Javaid, U., and Xia, Y. (2024, January 6\u201310). Adaptive Backstepping Integral Sliding Mode Control of Multirotor UAV System Used for Smart Agriculture. Proceedings of the 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia.","DOI":"10.1109\/SMC54092.2024.10831925"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1504\/IJVP.2018.088783","article-title":"Advanced Control Techniques for Unmanned Ground Vehicle: Literature Survey","volume":"4","author":"Mohamed","year":"2018","journal-title":"Int. J. Veh. Perform."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"77689","DOI":"10.1109\/ACCESS.2023.3297511","article-title":"Road Recognition and Stability Control for Unmanned Ground Vehicles on Complex Terrain","volume":"11","author":"Ao","year":"2023","journal-title":"IEEE Access"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Wang, Q., He, J., Lu, C., Wang, C., Lin, H., Yang, H., Li, H., and Wu, Z. (2023). Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges. Appl. Sci., 13.","DOI":"10.3390\/app13127155"},{"key":"ref_184","first-page":"1","article-title":"Adaptive Dynamic Programming for Robust Path Tracking in an Agricultural Robot Using Critic Neural Networks","volume":"80","author":"Azimi","year":"2025","journal-title":"Agric. Eng."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"120254","DOI":"10.1016\/j.eswa.2023.120254","article-title":"Path Planning Techniques for Mobile Robots: Review and Prospect","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Utstumo, T., Berge, T.W., and Gravdahl, J.T. (2015, January 17\u201319). Non-Linear Model Predictive Control for Constrained Robot Navigation in Row Crops. Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain.","DOI":"10.1109\/ICIT.2015.7125124"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1109\/TCST.2023.3291533","article-title":"Local Navigation and Obstacle Avoidance for an Agricultural Tractor With Nonlinear Model Predictive Control","volume":"31","author":"Soitinaho","year":"2023","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"110219","DOI":"10.1016\/j.compag.2025.110219","article-title":"Path Tracking Control of Crawler Tractor Based on Adaptive Adjustment of Lookahead Distance Using Sparrow Search Algorithm","volume":"234","author":"Song","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Wen, J., Yao, L., Zhou, J., Yang, Z., Xu, L., and Yao, L. (2025). Path Tracking Control of Agricultural Automatic Navigation Vehicles Based on an Improved Sparrow Search-Pure Pursuit Algorithm. Agriculture, 15.","DOI":"10.3390\/agriculture15111215"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Hoffmann, G.M., Tomlin, C.J., Montemerlo, M., and Thrun, S. (2007, January 9\u201313). Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing. Proceedings of the 2007 American Control Conference, New York, NY, USA.","DOI":"10.1109\/ACC.2007.4282788"},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhai, Z., Zhu, Z., and Mao, E. (2022). Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm. Actuators, 11.","DOI":"10.3390\/act11010022"},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Sun, Y., Cui, B., Ji, F., Wei, X., and Zhu, Y. (2022). The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model. Appl. Sci., 12.","DOI":"10.3390\/app12157683"},{"key":"ref_193","doi-asserted-by":"crossref","unstructured":"Cui, B., Cui, X., Wei, X., Zhu, Y., Ma, Z., Zhao, Y., and Liu, Y. (2024). Design and Testing of a Tractor Automatic Navigation System Based on Dynamic Path Search and a Fuzzy Stanley Model. Agriculture, 14.","DOI":"10.3390\/agriculture14122136"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"104272","DOI":"10.1016\/j.compind.2025.104272","article-title":"Dynamic Obstacle Avoidance Control Based on a Novel Dynamic Window Approach for Agricultural Robots","volume":"167","author":"Wang","year":"2025","journal-title":"Comput. Ind."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"181","DOI":"10.35633\/inmateh-62-19","article-title":"Intelligent Control Technology Of Agricultural Greenhouse Operation Robot Based On Fuzzy Pid Path Tracking Algorithm","volume":"62","author":"Qun","year":"2020","journal-title":"INMATEH Agric. Eng."},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Jiao, J., Chen, J., Qiao, Y., Wang, W., Wang, C., and Gu, L. (2018, January 26\u201329). Single Neuron PID Control of Agricultural Robot Steering System Based on Online Identification. Proceedings of the 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), Bamberg, Germany.","DOI":"10.1109\/BigDataService.2018.00036"},{"key":"ref_197","doi-asserted-by":"crossref","unstructured":"G\u00f6k\u00e7e, B., Koca, Y.B., Aslan, Y., and G\u00f6k\u00e7e, C.O. (2021). Particle Swarm Optimization-Based Optimal PID Control of an Agricultural Mobile Robot, \u201cProf. Marin Drinov\u201d Publishing House of Bulgarian Academy of Sciences.","DOI":"10.7546\/CRABS.2021.04.12"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1729881419897678","DOI":"10.1177\/1729881419897678","article-title":"Feedforward-plus-Proportional\u2013Integral\u2013Derivative Controller for Agricultural Robot Turning in Headland","volume":"17","author":"Huang","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"035045","DOI":"10.1063\/5.0186600","article-title":"Fuzzy Adaptive PID Control for Path Tracking of Field Intelligent Weeding Machine","volume":"14","author":"Liu","year":"2024","journal-title":"AIP Adv."},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Mekonen, E.A., Kassahun, E., Tigabu, K., Bekele, M., and Yehule, A. (2024, January 18\u201320). Model Predictive Controller Design for Precision Agricultural Robot. Proceedings of the 2024 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), Bahir Dar, Ethiopia.","DOI":"10.1109\/ICT4DA62874.2024.10777188"},{"key":"ref_201","doi-asserted-by":"crossref","unstructured":"Rakovi\u0107, S.V., and Levine, W.S. (2019). Learning-Based Fast Nonlinear Model Predictive Control for Custom-Made 3D Printed Ground and Aerial Robots. Handbook of Model Predictive Control, Springer International Publishing.","DOI":"10.1007\/978-3-319-77489-3"},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s10514-020-09915-y","article-title":"High Precision Control and Deep Learning-Based Corn Stand Counting Algorithms for Agricultural Robot","volume":"44","author":"Zhang","year":"2020","journal-title":"Auton. Robots"},{"key":"ref_203","first-page":"155","article-title":"Autonomous Travel of Lettuce Harvester Using Model Predictive Control","volume":"52","author":"Mitsuhashi","year":"2019","journal-title":"IFAC-Pap."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1007\/s10846-020-01257-2","article-title":"Path Tracking Control for Autonomous Harvesting Robots Based on Improved Double Arc Path Planning Algorithm","volume":"100","author":"Wang","year":"2020","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"9296","DOI":"10.1109\/LRA.2025.3592100","article-title":"Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots","volume":"10","author":"Kulathunga","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"108527","DOI":"10.1016\/j.compeleceng.2022.108527","article-title":"Fault-Tolerant Control Based on Fractional Sliding Mode: Crawler Plant Protection Robot","volume":"105","author":"Li","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Jiao, J., Wang, W., He, Y., Wu, Y., Zhang, F., and Gu, L. (2019). Adaptive Fuzzy Sliding Mode-Based Steering Control of Agricultural Tracked Robot. Fuzzy Systems and Data Mining V, IOS Press.","DOI":"10.3233\/FAIA190187"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"108089","DOI":"10.1016\/j.compeleceng.2022.108089","article-title":"A Deep Reinforcement Learning-Based Multi-Agent Area Coverage Control for Smart Agriculture","volume":"101","author":"Din","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1177\/00202940231164125","article-title":"Single-Layer Neural-Network Based Control of Agricultural Mobile Robot","volume":"56","year":"2023","journal-title":"Meas. Control"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"106920","DOI":"10.1016\/j.compag.2022.106920","article-title":"Unmanned Airboat Technology and Applications in Environment and Agriculture","volume":"197","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Xu, Q. (2014, January 8\u201310). USV Course Controller Optimization Based on Elitism Estimation of Distribution Algorithm. Proceedings of the 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China.","DOI":"10.1109\/CGNCC.2014.7007338"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1515\/pomr-2017-0001","article-title":"Path Following Control of the Underactuated USV Based On the Improved Line-of-Sight Guidance Algorithm","volume":"24","author":"Liu","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"102759","DOI":"10.1016\/j.apor.2021.102759","article-title":"A Path Planning Strategy Unified with a COLREGS Collision Avoidance Function Based on Deep Reinforcement Learning and Artificial Potential Field","volume":"113","author":"Li","year":"2021","journal-title":"Appl. Ocean Res."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Hu, C., Zhu, C., Zhu, Y., and Sheng, Y. (2021). An Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay for Path Planning of Unmanned Surface Vehicles. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9111267"},{"key":"ref_215","unstructured":"Md. Zain, Z., Ismail, Z.H., Li, H., Xiang, X., and Karri, R.R. Control System Development of Unmanned Surface Vehicles (USVs) with Fuzzy Logic Controller. Proceedings of the 13th National Technical Seminar on Unmanned System Technology 2023\u2014Volume 2."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"11553","DOI":"10.3182\/20140824-6-ZA-1003.00616","article-title":"Development of an Unmanned Surface Vehicle Platform for Autonomous Navigation in Paddy Field","volume":"47","author":"Liu","year":"2014","journal-title":"IFAC Proc. Vol."},{"key":"ref_217","unstructured":"Temilolorun, A., and Singh, Y. (2024). Towards Design and Development of a Low-Cost Unmanned Surface Vehicle for Aquaculture Water Quality Monitoring in Shallow Water Environments. arXiv."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1021\/acsestwater.1c00342","article-title":"Sensor-Equipped Unmanned Surface Vehicle for High-Resolution Mapping of Water Quality in Low- to Mid-Order Streams","volume":"2","author":"Griffiths","year":"2022","journal-title":"ACS EST Water"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"104852","DOI":"10.1016\/j.engappai.2022.104852","article-title":"An Evolutionary Multi-Objective Path Planning of a Fleet of ASVs for Patrolling Water Resources","volume":"112","author":"Peralta","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"91","DOI":"10.13031\/ja.15796","article-title":"Towards Autonomous, Optimal Water Sampling with Aerial and Surface Vehicles for Rapid Water Quality Assessment","volume":"67","author":"Nguyen","year":"2024","journal-title":"J. ASABE"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"110119","DOI":"10.1016\/j.compag.2025.110119","article-title":"Analysis and Realization of a Self-Adaptive Grasper Grasping for Non-Destructive Picking of Fruits and Vegetables","volume":"232","author":"Huang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"110131","DOI":"10.1016\/j.compag.2025.110131","article-title":"AI-Driven Adaptive Grasping and Precise Detaching Robot for Efficient Citrus Harvesting","volume":"232","author":"Yoo","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"106394","DOI":"10.1016\/j.conengprac.2025.106394","article-title":"A Comprehensive Control Architecture for Semi-Autonomous Dual-Arm Robots in Agriculture Settings","volume":"163","author":"Palmieri","year":"2025","journal-title":"Control Eng. Pract."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"108938","DOI":"10.1016\/j.compag.2024.108938","article-title":"Robotic Arms in Precision Agriculture: A Comprehensive Review of the Technologies, Applications, Challenges, and Future Prospects","volume":"221","author":"Jin","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Kolhalkar, N.R., Pandit, A.A., Kedar, S.A., and Yedukondalu, G. (2025, January 16\u201317). Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce. Proceedings of the 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET), Pune, India.","DOI":"10.1109\/ICAET63349.2025.10932313"},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Jin, T., Han, X., Wang, P., Lyu, Y., Chang, E., Jeong, H., and Xiang, L. (2025). Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard. Agriculture, 15.","DOI":"10.3390\/agriculture15151593"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Ali Hassan, M., Cao, Z., and Man, Z. (2022, January 24\u201325). End Effector Position Control of Pantograph Type Robot Using Sliding Mode Controller. Proceedings of the 2022 Australian & New Zealand Control Conference (ANZCC), Gold Coast, Australia.","DOI":"10.1109\/ANZCC56036.2022.9966971"},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"26294","DOI":"10.1109\/ACCESS.2022.3157600","article-title":"Trajectory Control of Two-Degree-of-Freedom Sweet Potato Transplanting Robot Arm","volume":"10","author":"Liu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Mueangprasert, M., Chermprayong, P., and Boonlong, K. (2023, January 18\u201320). Robot Arm Movement Control by Model-Based Reinforcement Learning Using Machine Learning Regression Techniques and Particle Swarm Optimization. Proceedings of the 2023 Third International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), Bangkok, Thailand.","DOI":"10.1109\/ICA-SYMP56348.2023.10044940"},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"107952","DOI":"10.1016\/j.compag.2023.107952","article-title":"A Bionic Adaptive End-Effector with Rope-Driven Fingers for Pear Fruit Harvesting","volume":"211","author":"Li","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"111043","DOI":"10.1016\/j.compag.2025.111043","article-title":"A Super-Hydrophobic Tactile Sensor for Damage-Free Fruit Grasping","volume":"239","author":"Wei","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_232","doi-asserted-by":"crossref","unstructured":"Kumar, S., Mohan, S., and Skitova, V. (2023). Designing and Implementing a Versatile Agricultural Robot: A Vehicle Manipulator System for Efficient Multitasking in Farming Operations. Machines, 11.","DOI":"10.3390\/machines11080776"},{"key":"ref_233","doi-asserted-by":"crossref","unstructured":"Sriram, A., R, A.R., Krishnan, R., Jagadeesh, S., and Gnanasekaran, K. (2023, January 29\u201330). IoT-Enabled 6DOF Robotic Arm with Inverse Kinematic Control: Design and Implementation. Proceedings of the 2023 IEEE World Conference on Applied Intelligence and Computing (AIC), Virtual.","DOI":"10.1109\/AIC57670.2023.10263943"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40648-022-00233-9","article-title":"Automated Harvesting by a Dual-Arm Fruit Harvesting Robot","volume":"9","author":"Yoshida","year":"2022","journal-title":"ROBOMECH J."},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Mapes, J., Dai, A., Xu, Y., and Agehara, S. (2021, January 5\u20137). Harvesting End-Effector Design and Picking Control. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA.","DOI":"10.1109\/SSCI50451.2021.9659872"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"64","DOI":"10.20965\/jrm.2025.p0064","article-title":"Editorial Office Development of 2-DOF Manipulator Using Straight-Fiber-Type Pneumatic Artificial Muscle for Agriculture","volume":"37","author":"Seno","year":"2025","journal-title":"J. Robot. Mechatron."},{"key":"ref_237","unstructured":"(2025, July 11). MarketsandMarkets Smart Agriculture Market Size, Share and Trends, 2025. Available online: https:\/\/www.marketsandmarkets.com\/Market-Reports\/smart-agriculture-market-239736790.html."},{"key":"ref_238","doi-asserted-by":"crossref","unstructured":"La Rocca, P., Guennebaud, G., and Bugeau, A. (2025). To What Extent Can Current French Mobile Network Support Agricultural Robots?. arXiv.","DOI":"10.1109\/ICT4S68164.2025.00031"},{"key":"ref_239","unstructured":"IEEE Standards Association (Standard No. IEEE 802.15.4-2020). Available online: https:\/\/standards.ieee.org\/ieee\/802.15.4\/7029\/."},{"key":"ref_240","doi-asserted-by":"crossref","unstructured":"Aldhaheri, L., Alshehhi, N., Manzil, I.I.J., Khalil, R.A., Javaid, S., Saeed, N., and Alouini, M.-S. (2024). LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2024.3486369"},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Zhivkov, T., and Sklar, E.I. (2022). 5g on the Farm: Evaluating Wireless Network Capabilities for Agricultural Robotics. arXiv.","DOI":"10.3390\/machines11121064"},{"key":"ref_242","unstructured":"(2025, October 26). Bluetooth\/BLE Core Specification. Bluetooth\u00ae Technol. Website 2024. Available online: https:\/\/www.bluetooth.com\/specifications\/specs\/core-specification-6-0\/."},{"key":"ref_243","unstructured":"IEEE Standards Association (Standard No. IEEE 802.11ax-2021). Available online: https:\/\/standards.ieee.org\/ieee\/802.11ax\/7180\/."},{"key":"ref_244","first-page":"b254","article-title":"A Case Study: A Review On Agriculture Robot","volume":"11","author":"Ahmad","year":"2024","journal-title":"J. Emerg. Technol. Innov. Res."},{"key":"ref_245","first-page":"28","article-title":"IoT-Enabled LoRaWAN Gateway for Monitoring and Predicting Spatial Environmental Parameters in Smart Greenhouses: A Review","volume":"7","author":"Bicamumakuba","year":"2025","journal-title":"Precis. Agric. Sci. Technol."},{"key":"ref_246","unstructured":"Bailey, J.K. (2025). IoT and Generative AI for Enhanced Data-Driven Agriculture. [Ph.D. Thesis, Purdue University Graduate School]."},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Nair, K.K., Abu-Mahfouz, A.M., and Lefophane, S. (2019, January 6\u20138). Analysis of the Narrow Band Internet of Things (NB-IoT) Technology. Proceedings of the 2019 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa.","DOI":"10.1109\/ICTAS.2019.8703630"},{"key":"ref_248","doi-asserted-by":"crossref","unstructured":"Lauridsen, M., Vejlgaard, B., Kovacs, I.Z., Nguyen, H., and Mogensen, P. (2017, January 19\u201322). Interference Measurements in the European 868 MHz ISM Band with Focus on LoRa and SigFox. Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925650"},{"key":"ref_249","unstructured":"(2025, October 26). LTE-M Global LTE-M Connectivity | Emnify. Available online: https:\/\/www.emnify.com\/iot-supernetwork\/global-iot-coverage\/lte-m."},{"key":"ref_250","unstructured":"(2025, October 26). RPMA\u2014The World\u2019s Premier IoT Solutions Provider. Available online: https:\/\/rpmanetworks.com\/."},{"key":"ref_251","unstructured":"(2025, October 26). WavIoT WAVIoT\u2014LPWAN Solutions for IoT and M2M. Available online: https:\/\/waviot.com\/."},{"key":"ref_252","unstructured":"(2025, October 26). 4G\/LTE-Advanced LTE vs LTE Advanced: Is 4G LTE Different from LTE Advanced?\u2014Commsbrief. Available online: https:\/\/commsbrief.com\/lte-vs-lte-advanced-is-4g-lte-different-from-lte-advanced\/."},{"key":"ref_253","unstructured":"Akhila, S. (2023, January 15\u201317). Hemavathi 5G Ultra-Reliable Low-Latency Communication: Use Cases, Concepts and Challenges. Proceedings of the 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_254","doi-asserted-by":"crossref","unstructured":"Kim, H. (2020). Enhanced Mobile Broadband Communication Systems*. Design and Optimization for 5G Wireless Communications, IEEE.","DOI":"10.1002\/9781119494492.ch7"},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MNET.2017.8120237","article-title":"Massive Machine-Type Communications","volume":"31","author":"Dutkiewicz","year":"2017","journal-title":"IEEE Netw."},{"key":"ref_256","unstructured":"Informed, T. (2025, July 11). Agriculture Gets Boost from Bots and Portable 5G Network. Available online: https:\/\/techinformed.com\/agriculture-gets-boost-from-bots-and-portable-5g-network\/."},{"key":"ref_257","unstructured":"Dresden, T.U. (2025, July 11). Digitalization for a Sustainable Future in Agriculture: Successful Completion of the LANDNETZ Collaborative Project. Available online: https:\/\/tu-dresden.de\/ing\/maschinenwesen\/die-fakultaet\/news\/digitalisierung-fuer-eine-nachhaltigere-landwirtschaft-der-zukunft-erfolgreicher-abschluss-des-verbundprojektes-landnetz?set_language=en."},{"key":"ref_258","unstructured":"Lindenschmitt, D., Fischer, C., Haussmann, S., Kalter, M., Kallfass, I., and Schotten, H. (2024). Agricultural On-Demand Networks for 6G Enabled by THz Communication. arXiv."},{"key":"ref_259","doi-asserted-by":"crossref","unstructured":"Cheraghi, A.R., Shahzad, S., and Graffi, K. (2021, January 2\u20133). Past, Present, and Future of Swarm Robotics. Proceedings of the SAI Intelligent Systems Conference, Virtual.","DOI":"10.1007\/978-3-030-82199-9_13"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/11\/159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:28:22Z","timestamp":1761888502000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/14\/11\/159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":259,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["robotics14110159"],"URL":"https:\/\/doi.org\/10.3390\/robotics14110159","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]}}}