Machine Learning (AutoML)-Driven Wheat Yield Prediction for European Varieties: Enhanced Accuracy Using Multispectral UAV Data

Key Points

• This research aims to improve the accuracy of wheat yield predictions for European varieties using machine learning techniques.
• Utilized AutoML algorithms to analyze data
• Incorporated multispectral data from UAVs
• Focused on various European wheat varieties
• Employed statistical methods for validation
• Significantly improved yield prediction accuracy compared to traditional methods
• Demonstrated the effectiveness of multispectral data in enhancing predictions
• Highlighted variations in yield among different wheat varieties