| _ | lodging | _ |
|---|
| Assessing | lodging | Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images |
| Assessing the Self-Recovery Ability of Maize after | lodging | Using UAV-LiDAR Data |
| Classification of Crop | lodging | with Gray Level Co-occurrence Matrix |
| Decision-Tree Approach to Identifying Paddy Rice | lodging | with Multiple Pieces of Polarization Information Derived from Sentinel-1, A |
| Detection and Analysis of Degree of Maize | lodging | Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods |
| Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and | lodging | Resistance |
| Discriminant analysis for | lodging | severity classification in wheat using RADARSAT-2 and Sentinel-1 data |
| Efficient Wheat | lodging | Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework |
| Extraction of Sunflower | lodging | Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning |
| Identifying Corn | lodging | in the Mature Period Using Chinese GF-1 PMS Images |
| Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict | lodging | Damage in Sorghum |
| Landscape-Scale Crop | lodging | Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images |
| Mapping Barley | lodging | with UAS Multispectral Imagery and Machine Learning |
| Prediction of Areal Soybean | lodging | Using a Main Stem Elongation Model and a Soil-Adjusted Vegetation Index That Accounts for the Ratio of Vegetation Cover |
| Quantifying | lodging | Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach |
| Quantifying | lodging | Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach |
| Quantifying | lodging | Percentage, Lodging Development and Lodging Severity Using a Uav-based Canopy Height Model |
| Quantifying | lodging | Percentage, Lodging Development and Lodging Severity Using a Uav-based Canopy Height Model |
| Quantifying | lodging | Percentage, Lodging Development and Lodging Severity Using a Uav-based Canopy Height Model |
| Quantitative Identification of Maize | lodging | -Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation |
| Quantitative Monitoring Method for Determining Maize | lodging | in Different Growth Stages, A |
| Remote sensing-based crop | lodging | assessment: Current status and perspectives |
| Risk Assessment of Different Maize (Zea mays L.) | lodging | Types in the Northeast and the North China Plain Based on a Joint Probability Distribution Model |
| Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice | lodging | Identification in Multi-date UAV Visible Images |
| Spatial and Spectral Hybrid Image Classification for Rice | lodging | Assessment through UAV Imagery |
| Understanding of Crop | lodging | Induced Changes In Scattering Mechanisms Using Radarsat-2 and Sentinel-1 Derived Metrics |
| Wheat | lodging | Assessment Using Multispectral UAV Data |
| Wheat | lodging | Detection from UAS Imagery Using Machine Learning Algorithms |
| Winter Wheat | lodging | Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery |
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