Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms

Keyphrases

• Agronomists 50%
• Artificial Neural Network 50%
• Configuration Parameters 25%
• Crop Prediction 25%
• Crop Yield Prediction 75%
• Decision Support System 25%
• Error Value 75%
• Field Survey Data 25%
• First Crop 75%
• Food Security 25%
• Government Statistics 25%
• Growing Season 25%
• Harvesting Period 25%
• Hindcasts 25%
• Image Compositing 100%
• Machine Learning 25%
• Machine Learning Algorithms 100%
• Machine Learning Models 25%
• Machine Learning Techniques 25%
• Mean Absolute Percentage Error 75%
• Model Configuration 25%
• Model Validation 25%
• Official Statistics 25%
• Percentage Error 75%
• Predicted Yield 25%
• Random Forest 50%
• Rice Yield 50%
• Rice Yield Prediction 100%
• Root Mean Square 75%
• Satellite Images 25%
• Second Crop 75%
• Security Issues 25%
• Sentinel-2 100%
• Sentinel-2 Images 50%
• Support Vector Machine 75%
• Support Vector Machine Model 50%
• Taiwan 25%
• Three-machine 25%
• Yield Modeling 50%
• Yield Prediction 100%

Agricultural and Biological Sciences

• Agronomists 40%
• Growing Season 20%
• Model Validation 20%
• Neural Network 40%
• Ripening 20%
• Support Vector Machine 100%
• Transplanting 20%

Food Science

• Food Security 100%