Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges
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| Udgivet i: | Plants vol. 14, no. 16 (2025), p. 2544-2574 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2223-7747 | ||
| 024 | 7 | |a 10.3390/plants14162544 |2 doi | |
| 035 | |a 3244051177 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231551 |2 nlm | ||
| 100 | 1 | |a Yang, Xiaofei |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 245 | 1 | |a Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Physiology | ||
| 653 | |a Dielectric properties | ||
| 653 | |a Data acquisition | ||
| 653 | |a Data processing | ||
| 653 | |a Modelling | ||
| 653 | |a Remote sensing | ||
| 653 | |a Water depth | ||
| 653 | |a Water | ||
| 653 | |a Remote monitoring | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Agriculture | ||
| 653 | |a Image processing | ||
| 653 | |a Spatial discrimination | ||
| 653 | |a Data integration | ||
| 653 | |a Radiation | ||
| 653 | |a Efficiency | ||
| 653 | |a Nitrogen | ||
| 653 | |a Aircraft | ||
| 653 | |a Stitching | ||
| 653 | |a Vegetation | ||
| 653 | |a Crops | ||
| 653 | |a Crop growth | ||
| 653 | |a Image segmentation | ||
| 653 | |a Vegetation index | ||
| 653 | |a Nutrient status | ||
| 653 | |a Spatial resolution | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Aerodynamics | ||
| 653 | |a Sensors | ||
| 653 | |a Radiometric correction | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Chen, Junying |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Lu Xiaohan |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Liu, Hao |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Liu, Yanfu |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Bai Xuqian |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Long, Qian |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 700 | 1 | |a Zhang, Zhitao |u College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China; xiaofei-yang@nwafu.edu.cn (X.Y.); | |
| 773 | 0 | |t Plants |g vol. 14, no. 16 (2025), p. 2544-2574 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244051177/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244051177/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244051177/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |