Remote and Proximal Sensing for Arable Crops Monitoring and Yield Assessment

Dear Colleagues,

Remote and proximal sensing technologies have rapidly transformed arable crop monitoring over the last two decades, evolving from simple spectral index analysis to sophisticated multi-sensor, multi-scale, and AI-driven approaches. Recent advancements in satellite missions, UAV multispectral/thermal imaging, LiDAR, proximal canopy sensors, and soil–plant–atmosphere monitoring systems have enabled unprecedented spatial and temporal detail in assessing crop conditions, stress responses, and yield formation processes.

This Special Issue aims to bring together cutting-edge research that leverages Earth observation, field-based sensing, artificial intelligence, and data-fusion methodologies to improve crop monitoring, input optimization, and yield assessment in arable systems. We invite contributions showcasing methodological innovations, operational applications, and integrative frameworks that support sustainable crop production and precision agriculture.

We particularly welcome studies that combine multi-scale datasets, exploit machine learning or deep learning models, develop decision-support tools, or demonstrate real-world implementations.

We seek original research articles, technical developments, reviews, and case studies covering remote sensing, proximal sensing, AI analytics, data fusion, variable-rate applications, and yield prediction for major arable crops (e.g., cereals, cotton, maize, legumes). Submissions addressing climate-smart agriculture, resource-efficient management, and regional upscaling of field-level monitoring approaches are especially encouraged.

Dr. Emmanouil Psomiadis
Prof. Dr. Nicholas Dercas
Guest Editors

Manuscript Submission Information

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