Plant disease can cause quality and quantity reduction of agriculture crops. In some countries, farmers spend considerable time to consult with plant pathologists, as time is an important factor to control disease; so it seems necessary to offer a fast, cheap and accurate method to detect plant diseases. Since the fungal diseasesnamed ‘Powdery Mildew’ and ‘Anthracnose’ cause the greatest amount of damage in cucumber produced in greenhouses, thus in this research the two mentioned fungal diseases detection and classification were studied using image processing and neural networks techniques. Image processing include 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 areas of the leaf and 4) extraction of textural properties of defected areas 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 were selected as the best model that was able to successfully detect and classify the mentioned plant diseases in 6 seconds with 99.96% accuracy.