WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

30 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. Automatic grading evaluation of winter wheat lodging based on deep learning H. Zang et al. https://doi.org/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 https://doi.org/10.3390/agriculture15151570
4. 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
5. Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network J. Yu et al. https://doi.org/10.3390/drones7020143
6. Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery Z. Tang et al. https://doi.org/10.3390/rs14194887
7. Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data S. Chauhan et al. https://doi.org/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. https://doi.org/10.1016/j.compag.2022.106804
9. A Novel Approach to Estimate Maize Lodging Area With PolSAR Data H. Guan et al. https://doi.org/10.1109/TGRS.2022.3216341
10. Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands M. Dubbini et al. https://doi.org/10.3390/rs14041028
11. Wheat lodging extraction using Improved_Unet network J. Yu et al. https://doi.org/10.3389/fpls.2022.1009835
12. Remote sensing-based rice stress and health assessment: current and future perspectives Z. Wang et al. https://doi.org/10.1080/01431161.2026.2612851
13. LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming N. Ali et al. https://doi.org/10.3389/fpls.2023.1255961
14. Assessment of Soybean Lodging Using UAV Imagery and Machine Learning S. Sarkar et al. https://doi.org/10.3390/plants12162893
15. Validating the contributions of stem and root traits to barley lodging in western Canada M. Taylor et al. https://doi.org/10.3389/fpls.2025.1547207
16. 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
17. 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
18. 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
19. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex https://doi.org/10.3390/drones3030074
20. 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
21. 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
22. Towards CNN-based classification of crop independent agricultural sub-classes L. Arslanova et al. https://doi.org/10.1016/j.atech.2025.101284
23. High-throughput proximal ground crop phenotyping systems – A comprehensive review Z. Rui et al. https://doi.org/10.1016/j.compag.2024.109108
24. 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
25. 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
26. 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
27. Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms Z. Zhang et al. https://doi.org/10.3390/rs12111838
28. 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
29. Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms S. Sharda et al. https://doi.org/10.1038/s41598-025-09109-5
30. 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