A recognition system to detect Powdery Mildew and Anthracnose fungal disease of cucumber leaf using image processing and artificial neural networks technique

Plant disease can cause quality and quantity reduction of agriculture crops. In some countries farmers spend considerable time to consult with plant pathologists, whereas time is an important factor to control disease, so it seems to offer a fast, cheap and accurate method to detect plant diseases. Since the fungal disease named ‘Powdery Mildew’ and ‘Anthracnose’ cause the greatest amount of damage in cucumber produced in greenhouses, thus in this research the mentioned two fungal disease detection and classification were studied using image processing and neural networks technique. Image processing steps includes four main steps: 1) Image acquisition 2) preprocessing 3) extraction of the best color parameters of HSV and L*a*b* color spaces in order to classify and extract defected area of the leaf and 4) extraction of textural properties of defected area of cucumber leaf using co-occurrence matrix. Since, two factors of accuracy and time are important in detection and classification of plant disease, thus artificial neural networks (ANN) with back propagation algorithm (BP) and Levenberg-Marquardt (LM) training function was selected as the best model that was able to successfully detect and classify mentioned plant diseases in 6 seconds with 99.96% accuracy.