{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T14:47:33Z","timestamp":1784386053969,"version":"3.55.0"},"reference-count":108,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Global agricultural systems face unprecedented challenges from climate change, resource scarcity, and rising food demand, requiring transformative solutions. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a critical tool for agricultural monitoring, yet a systematic synthesis of its applications remains understudied. This paper presents a comprehensive bibliometric and knowledge graph analysis of 650\u202f+\u202fpublications (2000\u20132024) to map AI\u2019s role in agricultural information identification, with emphasis on DL and remote sensing integration (e.g., UAVs, satellites). Results highlight Convolutional Neural Networks (CNNs) as the dominant technology for real-time crop monitoring but reveal three persistent barriers: (1) scarcity of annotated datasets, (2) poor model generalization across environments, and (3) challenges in fusing multi-source data. Crucially, interdisciplinary collaboration\u2014though vital for scalability\u2014is identified as an underdeveloped research frontier. It is concluded that while AI can revolutionize agriculture, its potential hinges on improving data quality, developing environment-adaptive models, and fostering cross-domain partnerships. This study provides a strategic framework to accelerate AI\u2019s integration into global agricultural systems, addressing both technical gaps and policy needs for future food security.<\/jats:p>","DOI":"10.3389\/frai.2025.1636898","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T05:40:23Z","timestamp":1758087623000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A bibliometric review of deep learning in crop monitoring: trends, challenges, and future perspectives"],"prefix":"10.3389","volume":"8","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lige","family":"Wuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linnan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"ref1","volume-title":"Applications of Drones in Emerging Economies: a case study of Malaysia","author":"Ab Rahman","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"106279","DOI":"10.1016\/j.compag.2021.106279","article-title":"Tomato plant disease detection using transfer learning with C-GAN synthetic images","volume":"187","author":"Abbas","year":"2021","journal-title":"Comput. 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