A bibliometric review of deep learning in crop monitoring: trends, challenges, and future perspectives

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 + publications (2000–2024) to map AI’s 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—though vital for scalability—is 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’s integration into global agricultural systems, addressing both technical gaps and policy needs for future food security.