Improving soybean yield prediction by integrating UAV nadir and cross-circling oblique imaging

Keyphrases

• UAV 100%
• Oblique Imaging 100%
• Soybean Yield Prediction 100%
• Three-dimensional (3D) 80%
• Shapley Value 60%
• Yield Prediction 60%
• Oblique Photography 60%
• Meta-learner 40%
• Stacking Ensemble Learning 40%
• Canopy Structure 40%
• Modal Data 40%
• Bayesian Model Averaging 40%
• SfM-MVS 40%
• Random Forest 20%
• Two Dimensional 20%
• Structural Features 20%
• Estimation Accuracy 20%
• High-throughput 20%
• Multispectral Image 20%
• Multimodal Data 20%
• Machine Learning Models 20%
• Least Absolute Shrinkage and Selection Operator (LASSO) 20%
• Vegetation Index 20%
• Yield Estimation 20%
• Data Combination 20%
• Orthophoto 20%
• Point Cloud 20%
• Phenotypic Characteristics 20%
• Ridge Regression 20%
• Accurate Estimation 20%
• Soybean Yield 20%
• Soybean Breeding 20%
• Soybean Canopy 20%
• Ensemble Learning Model 20%
• Entropy Weight 20%
• Throughput Estimation 20%
• Stacking Method 20%
• Crop Canopy 20%
• Unmanned Aerial Vehicle (UAV) Imagery 20%
• Nadir Image 20%
• Three-dimensional Features 20%
• Breeding Material 20%
• Weighted Average Method 20%
• Texture Features 20%
• Spatial Structure Information 20%
• Ensemble Framework 20%
• XGBoost 20%

Earth and Planetary Sciences

• Pilotless Aircraft 100%
• Stacking 50%
• Machine Learning 16%
• Multispectral Image 16%
• Vegetation Index 16%
• Orthophoto 16%
• Multisensor Fusion 16%

Agricultural and Biological Sciences

• Unmanned Aerial Vehicle 100%
• Vegetation Index 16%