Synergistic Use of AI and Remote Sensing for Monitoring Crop Health and Optimizing Stress Management

Agricultural systems are facing mounting challenges as rapid global change intensifies the frequency and severity of extreme events that threaten crop health and food security. Both biotic stresses, such as pests and diseases, and abiotic stresses, including heatwaves and drought, are increasingly disrupting crop productivity. Recent advances in artificial intelligence (AI) and remote sensing have improved the monitoring of crop condition across broad spatial and temporal scales. AI-enabled phenotyping, satellite- and UAV-based assessments, and automated stress detection now offer high-throughput and cost-effective tools for crop surveillance in diverse environments. However, important gaps remain in integrating heterogeneous data sources, enabling real-time diagnosis, and linking predictive performance with a stronger understanding of crop physiological responses and plant–environment interactions.This Research Topic aims to advance integrative approaches that combine remote sensing, AI, and crop or plant system knowledge to improve crop health monitoring, stress diagnosis, and stress management under changing climatic and agronomic conditions. Particular emphasis is placed on frameworks that couple multi-source remote sensing and data-driven methods with process-based crop models to better interpret crop responses, support physiologically informed management decisions, and improve resilience in agricultural systems. Rather than focusing primarily on AI model interpretability alone, this Topic emphasizes system-level crop monitoring, plant–environment interactions, and the translation of stress intelligence into practical decision support for agriculture.By bridging empirical and mechanistic paradigms, contributors are encouraged to develop methodologies that improve the accuracy of crop health assessment, enable timely and precise stress management, and support crop resilience, yield stability, and resource-use efficiency. Key objectives include refining monitoring tools, strengthening cross-scale data integration, and establishing decision-support systems that respond dynamically to real-world agricultural challenges. Fundamental questions include how AI and process-based modeling can be combined to better capture crop stress responses, how stress events can be diagnosed in real or near-real time across scales, and how these advances can guide sustainable management strategies in crop production.This Research Topic welcomes contributions that advance the integration of AI and remote sensing in the monitoring and management of biotic and abiotic stresses in crop systems. Submissions should contribute to crop health assessment, stress detection, stress management, resilience, or resource-use efficiency in agricultural contexts. We welcome manuscript types accepted by Frontiers in Plant Science, including Original Research, Review, Methods, Opinion, and Perspective articles. Topics of interest include, but are not limited to:• AI-enabled phenotyping for crop stress detection and tolerance assessment• Satellite, proximal, and UAV sensing combined with AI for crop health monitoring and yield loss estimation• Multi-source data fusion approaches for diagnosis of biotic and abiotic stresses• Coupling AI with process-based crop models to reveal mechanisms of crop stress responses• Real-time or near-real-time decision-support systems for crop stress management• Optimization strategies for resource use, resilience, and sustainable crop production under variable climate conditionsWe welcome submissions of the following article types: Correction, Data Report, Editorial, FAIR² DATA Direct Submission, FAIR² Data, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Systematic Review, Technology and Code. Topic editors: Yang Song, Zhou Zhang, LIN TAO, Xiaoping Wang, Yahui Guo Source: https://www.frontiersin.org/research-topics/80220/synergistic-use-of-ai-and-remote-sensing-for-monitoring-crop-health-and-optimizing-stress-management