A Survey Disease Detection Mechanism for Cotton Leaf: Training & Precaution Based Approach

Abstract

The large number of people depends on cotton crop. The recognition of cotton leaf disease are of the major important as they have a cogent and momentous impact on quality and production of cotton. Cotton disease identification is an art and science. The start with collecting the images.We will consider two diseases they are Foliar, and Alternaria of cotton leaves. We have extracted the features and compare those features with the features that are extracted from the input test image they can like grayscaling, thresholding, cropping for detecting the boundary of image. Colour feature like HSV features are extracted from the output of segmentation and (ANN) Artificial neural network is trained by choosing the feature value that could distinguish the healthy and disease sample. Experimental result showed that classification performance by ANN taking feature set is better with an accuracy of 80%. The present work proposes a methodology for detecting cotton leaf disease early, using image processing techniques and artificial neural network (ANN). We are also work with the current and future precaution for the cotton tree to protects it from future disease & maintain it to improve its good production as well as life .