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Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges

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Publikašuvnnas:Plants vol. 14, no. 16 (2025), p. 2544-2574
Váldodahkki: Yang, Xiaofei
Eará dahkkit: Chen, Junying, Lu Xiaohan, Liu, Hao, Liu, Yanfu, Bai Xuqian, Long, Qian, Zhang, Zhitao
Almmustuhtton:
MDPI AG
Fáttát:
Physiology
Dielectric properties
Data acquisition
Data processing
Modelling
Remote sensing
Water depth
Water
Remote monitoring
Unmanned aerial vehicles
Agriculture
Image processing
Spatial discrimination
Data integration
Radiation
Efficiency
Nitrogen
Aircraft
Stitching
Vegetation
Crops
Crop growth
Image segmentation
Vegetation index
Nutrient status
Spatial resolution
Precision agriculture
Aerodynamics
Sensors
Radiometric correction
Algorithms
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Abstrákta:With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture.
ISSN:2223-7747
DOI:10.3390/plants14162544
Gáldu:Agriculture Science Database