Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

Abstract

The real-time detection of drought stress has major implications for preventing cash crop yield loss due to variable weather conditions and ongoing climate change. The most widely used indicator of drought sensitivity/tolerance in corn and soybean is the presence or absence of leaf wilting during periods of water stress. We develop a low-cost automated drought detection system using computer vision coupled with machine learning (ML) algorithms that document the drought response in corn and soybeans field crops. Using ML, we predict the drought status of crop plants with more than 80% accuracy relative to expert-derived visual drought ratings.

Topics & Concepts