WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

31 citations as recorded by crossref.

1. An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning H. Guan et al. https://doi.org/10.1016/j.jag.2022.102992
2. A breeding-oriented UAV phenotyping framework for scalable lodging assessment and candidate gene identification in soybean Z. Zhao et al. https://doi.org/10.1016/j.indcrop.2026.124188
3. Automatic grading evaluation of winter wheat lodging based on deep learning H. Zang et al. https://doi.org/10.3389/fpls.2024.1284861
4. SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment C. Guo & F. Tan https://doi.org/10.3390/agriculture15151570
5. Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images X. Yang et al. https://doi.org/10.3390/agriculture12070970
6. Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network J. Yu et al. https://doi.org/10.3390/drones7020143
7. Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery Z. Tang et al. https://doi.org/10.3390/rs14194887
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9. An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images L. Han et al. https://doi.org/10.1016/j.compag.2022.106804
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13. Remote sensing-based rice stress and health assessment: current and future perspectives Z. Wang et al. https://doi.org/10.1080/01431161.2026.2612851
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15. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning S. Sarkar et al. https://doi.org/10.3390/plants12162893
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17. Synergistic use of Sentinel-1 and Sentinel-2 data for Fall Armyworm infestation detection and mapping in maize croplands T. Dzurume et al. https://doi.org/10.1080/01431161.2025.2546155
18. Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management B. Zhao et al. https://doi.org/10.3390/agronomy10111762
19. Integration of artificial intelligence and remote sensing for crop yield prediction and crop growth parameter estimation in Mediterranean agroecosystems: Methodologies, emerging technologies, research gaps, and future directions W. Demissie et al. https://doi.org/10.1016/j.eja.2025.127894
20. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex https://doi.org/10.3390/drones3030074
21. Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning G. Li et al. https://doi.org/10.3390/rs13142721
22. A Decision-Tree Approach to Identifying Paddy Rice Lodging with Multiple Pieces of Polarization Information Derived from Sentinel-1 X. Dai et al. https://doi.org/10.3390/rs15010240
23. Towards CNN-based classification of crop independent agricultural sub-classes L. Arslanova et al. https://doi.org/10.1016/j.atech.2025.101284
24. High-throughput proximal ground crop phenotyping systems – A comprehensive review Z. Rui et al. https://doi.org/10.1016/j.compag.2024.109108
25. Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images O. Ajadi et al. https://doi.org/10.3390/rs12233885
26. Highly efficient wheat lodging extraction algorithm based on two-peak search algorithm X. Liu et al. https://doi.org/10.1007/s11119-025-10223-7
27. Determining rapeseed lodging angles and types for lodging phenotyping using morphological traits derived from UAV images C. Wang et al. https://doi.org/10.1016/j.eja.2024.127104
28. Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms Z. Zhang et al. https://doi.org/10.3390/rs12111838
29. Extraction and Mapping of Cropland Parcels in Typical Regions of Southern China Using Unmanned Aerial Vehicle Multispectral Images and Deep Learning S. Wu et al. https://doi.org/10.3390/drones7050285
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31. Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network D. Zhang et al. https://doi.org/10.1016/j.compag.2020.105845