Wheat Leaf Disease Detection Using Machine Learning: A Review

Today plant diseases are significant concern as it reduces the production and quality of agriculture produce. Most plant diseases are caused by bacteria, fungi and virus. Manual detection and identification of leaf disease involve more man power and expensive in large farm. Detection of disease and healthy monitoring of plant are major challenges for sustainable agriculture. Hence there is need to detect plant disease automatically using image processing technique at an early stage with more accuracy. It involves image acquisition, image preprocessing, image segmentation, feature extraction and classification of disease. To increase the quality of produce and yield, a regular monitoring technique for detection of diseases in plants becomes essential. The digital image processing system is one such powerful technique to diagnose the difficult symptoms much earlier than the human eye could recognize. It enables the farmers to take appropriate actions timely in order to safeguard the crop and get the desired quality and yield of agriculture produce. Different techniques used for the classification of plant disease using various classifiers such as Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors and other classifier methods have been discussed. The purpose of this paper is to give an overview of established methods for plant disease detection, classification systems