An Overview on Detection and Classification of Plant Diseases in Image Processing

In this paper; heterogeneous plant diseases that are feasible and their apprehension using contrasting techniques have been discussed. These techniques include Otsu method, image compression, image cropping and image denoising including K means clustering to articulate the disease images. Neural networks including back propagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) are also used to diagnose wheat and grape diseases. Cotton leaf diseases and rice plant disease using sobel operator, canny filter and feature extraction are passed down to recognize the disease. Many other diseases like orchid leaf disease, rubber tree leaf disease; apple fruit disease and chili plant disease can also be encountered using other approaches like fuzzy logic, Multi-class Support Vector Machine and Local Binary Pattern. A miniature explication on all the diseases and their detection has been given in this paper.