Crop Yield Prediction using Satellite/Uav Synergy and Machine Learning

Abstract

The work aims to predict soybean yield using satellite and Unmanned Aerial Vehicle (UAV) synergy and machine learning. UAV RGB imagery and Worldview satellite data was acquired in the summer of 2017 over a soybean field near Columbia, Missouri, USA. Canopy spectral features from satellite data and structural features from UAV imagery were combined and fused to predict soybean grain yield. Commonly used machine learning regression methods including Extreme Learning Regression (ELR), Random Forest Regression (RFR), Support Vector Regression (SVR) and Partial Least Squares Regression (PLSR) were utilized to predict soybean yield using spectral features from satellite data and structural features from UAV imagery. The results showed that canopy structure features such as canopy height and canopy coverage are important indicators for soybean grain yield estimation, and complementarities between Satellite and UAV data reveal a great potential of synergy, and lead to an improved performance for soybean grain yield estimation.