WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

28 citations as recorded by crossref.

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2. Automatic grading evaluation of winter wheat lodging based on deep learning H. Zang et al. 10.3389/fpls.2024.1284861
3. 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 10.3390/agriculture15151570
4. Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images X. Yang et al. 10.3390/agriculture12070970
5. Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network J. Yu et al. 10.3390/drones7020143
6. Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery Z. Tang et al. 10.3390/rs14194887
7. Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data S. Chauhan et al. 10.1016/j.rse.2020.111804
8. 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. 10.1016/j.compag.2022.106804
9. A Novel Approach to Estimate Maize Lodging Area With PolSAR Data H. Guan et al. 10.1109/TGRS.2022.3216341
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11. Wheat lodging extraction using Improved_Unet network J. Yu et al. 10.3389/fpls.2022.1009835
12. LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming N. Ali et al. 10.3389/fpls.2023.1255961
13. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning S. Sarkar et al. 10.3390/plants12162893
14. Validating the contributions of stem and root traits to barley lodging in western Canada M. Taylor et al. 10.3389/fpls.2025.1547207
15. Synergistic use of Sentinel-1 and Sentinel-2 data for Fall Armyworm infestation detection and mapping in maize croplands T. Dzurume et al. 10.1080/01431161.2025.2546155
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18. Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning G. Li et al. 10.3390/rs13142721
19. A Decision-Tree Approach to Identifying Paddy Rice Lodging with Multiple Pieces of Polarization Information Derived from Sentinel-1 X. Dai et al. 10.3390/rs15010240
20. Towards CNN-based classification of crop independent agricultural sub-classes L. Arslanova et al. 10.1016/j.atech.2025.101284
21. High-throughput proximal ground crop phenotyping systems – A comprehensive review Z. Rui et al. 10.1016/j.compag.2024.109108
22. Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images O. Ajadi et al. 10.3390/rs12233885
23. Highly efficient wheat lodging extraction algorithm based on two-peak search algorithm X. Liu et al. 10.1007/s11119-025-10223-7
24. Determining rapeseed lodging angles and types for lodging phenotyping using morphological traits derived from UAV images C. Wang et al. 10.1016/j.eja.2024.127104
25. Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms Z. Zhang et al. 10.3390/rs12111838
26. 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. 10.3390/drones7050285
27. Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms S. Sharda et al. 10.1038/s41598-025-09109-5
28. Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network D. Zhang et al. 10.1016/j.compag.2020.105845