Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods

This Special Issue, entitled Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods, brings together twelve peer-reviewed papers that advance remote sensing for crop management. The contributions span satellite and UAV platforms, multispectral and hyperspectral spectroscopy, radar, and LiDAR, with workflows that combine physics-based modelling, machine learning, and deep learning. Across cotton, wheat, rice, maize, soybean, and vineyards, the studies demonstrate the robust retrieval of canopy structure, leaf area, biomass, nitrogen uptake, water status, and grain quality, alongside field and crop mapping and early stress and disease characterisation. Methodological innovations include optimised UAV LiDAR acquisition and processing, time series feature selection and matching for crop mapping, uncertainty quantification for biophysical retrieval, and plant-level detection and panoptic recognition in RGB imagery and 3D point clouds. Together, this Reprint illustrates how scalable sensing and analytics support timely, data-driven decisions for more efficient and sustainable agriculture.