Innovative Leaf Disease Mapping: Unsupervised Anomaly Detection for Precise Area Estimation

Abstract Detecting and quantifying the diseased regions in a leaf is an important task in plant breeding in order to select plants based on disease resistance. Ratings done by eye and hand are not accurate, can be highly subjective and take a lot of work hours. Using machine learning, it is possible to generate faster more accurate data. By combining modern anomaly detection algorithms with masking algorithms a robust method capable of estimating the infected leaf area was developed, resulting in a novel method with superior results. Using unsupervised models both for the masking and for the detection, the method can easily be used for different kinds of detection tasks.