Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods (Hardcover)

Renan Falcioni

ISBN 10: 3725866104 / ISBN 13: 9783725866106

Published by Mdpi AG, 2026

About this Item

Hardcover. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9783725866106

Bibliographic Details

• Title: Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725866104
• ISBN 13: 9783725866106
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.