{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:09:11Z","timestamp":1788800951549,"version":"build-2803163510"},"reference-count":184,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qing Lan Project of Jiangsu Higher Education Institutions","award":["RC20251111"],"award-info":[{"award-number":["RC20251111"]}]},{"name":"Zhejiang Provincial Market Supervision Administration Key Scientific Research Project","award":["ZD2025017"],"award-info":[{"award-number":["ZD2025017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human\u2013machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise\u2014advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems.<\/jats:p>","DOI":"10.3390\/s26175680","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T16:30:02Z","timestamp":1788798602000},"page":"5680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Source Perception, Intelligent Decision-Making, and Precision Control for Autonomous Agricultural Systems: A Comprehensive Review"],"prefix":"10.3390","volume":"26","author":[{"given":"Shida","family":"Zhang","sequence":"first","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6217-088X","authenticated-orcid":false,"given":"Yong","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Xu","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Zheng","sequence":"additional","affiliation":[{"name":"Ningbo Fiber Inspection Institute, Ningbo Institute for Product and Food Quality Inspection, Ningbo 315000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6512","DOI":"10.1109\/COMST.2026.3708340","article-title":"A Comprehensive Survey on Cybersecurity Challenges and Defenses for Integrated Air, Ground, and Underwater Autonomous Vehicle Systems","volume":"28","author":"Ahmad","year":"2026","journal-title":"IEEE Commun. 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