About this Research Topic
The development of Remote Sensing (RS) and Artificial Intelligence (AI) techniques has drawn increasing interest and started a novel area of research applications in crop monitoring. RS imageries provide wide-source and real-time data for growth condition monitoring, pests, and diseases detection, etc., which are followed by the usage of AI for effective and efficient data mining to obtain new, insightful information to support practical guidance and further applications. Combining the advantages of RS and AI, automatic and fast processing and modeling of crop growth applications can be achieved. To build an RS-AI system for solving complex problems, researchers will comprehensively complete tasks from data acquisition to model construction, and achievements in any tasks will promote the development of RS-AI in crop monitoring.
In response to enhancing the mechanisms for monitoring crop growth, this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of AI and remote sensing. Research may address but are not limited to key technologies for monitoring the main food security impact factors supported by multi-source earth observation spatio-temporal big data and artificial intelligence to realize the monitoring of crop growth situation, and high-precision and accurate monitoring of key regions affected by food security. Research about the integration of multisource, multitemporal, or multiscale RS imageries (e.g., multispectral, hyperspectral, etc.), and AI such as deep learning, reinforcement learning, and federated learning, focusing on tackling the challenges or bottleneck problems faced in crop monitoring, such as growth condition monitoring, pests, and diseases detection, and applications are welcome.
Keywords:
•Data processing (multispectral, hyperspectral, thermal, LiDAR, etc.)
•Real-time object detection, counting, segmentation, and tracking
•Crop growth monitoring
•Pests, disease, and other disasters monitoring
•AI algorithms in agriculture
•Agriculture ecology
Article types:
Hypothesis & Theory, Methods, Mini Review, Original Research, Review, Technology and Code.
In response to enhancing the mechanisms for monitoring crop growth, this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of AI and remote sensing. Research may address but are not limited to key technologies for monitoring the main food security impact factors supported by multi-source earth observation spatio-temporal big data and artificial intelligence to realize the monitoring of crop growth situation, and high-precision and accurate monitoring of key regions affected by food security. Research about the integration of multisource, multitemporal, or multiscale RS imageries (e.g., multispectral, hyperspectral, etc.), and AI such as deep learning, reinforcement learning, and federated learning, focusing on tackling the challenges or bottleneck problems faced in crop monitoring, such as growth condition monitoring, pests, and diseases detection, and applications are welcome.
Keywords:
•Data processing (multispectral, hyperspectral, thermal, LiDAR, etc.)
•Real-time object detection, counting, segmentation, and tracking
•Crop growth monitoring
•Pests, disease, and other disasters monitoring
•AI algorithms in agriculture
•Agriculture ecology
Article types:
Hypothesis & Theory, Methods, Mini Review, Original Research, Review, Technology and Code.
Keywords: Data Processing, LiDAR, AI Algorithms, System Development, Agricultural Applications, Pests, Multisppectral, Hyperspectral
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
