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Detecting and monitoring the progress of powdery mildew disease in squash using hyperspectral imaging and artificial neural networks

Timely disease detection can improve disease management and reduce environmental impact, as applying unnecessary chemical applications could increase the risk of pollution and reduce profit. Herein, different development stages of powdery mildew disease (asymptomatic, early, intermediate, and late stage) were monitored in a squash crop by utilizing hyperspectral imaging (380-1030 nm) in laboratory conditions (the spectral measurements were conducted seven times). Radial basis function network was used to discriminate between healthy and diseased plant and to distinguish the infection level (disease development stage). Since, spectral Vegetation Indices (VIs) have been shown to be useful for monitoring disease development stages, seventeen VIs were selected and evaluated to detect and classify healthy and powdery mildew diseased plants. The best VI that can be used for disease detection was the water index (WI) in early, intermediate and late disease development stage. High classification rates, ranging from 82% 99% for asymptomatic to late stages, were achieved in disease detection. Keyword: Disease, powdery mildew, hyperspectral, vegetation indices, classification methods, neural networks.