Review of Classifiers Used for Identification and Classification of Plant Leaf Diseases

Abstract

The plant disease is vital problem in agriculture. The quality and the quantity of grains, spices, and food products we get through the plants rely on the plant’s health. The health of the plant is directly proportional to the profit we yield. The plant can have infections at anywhere in the part of it. The infections mostly on the leaf are evident proof. Most of the research work for the disease identification was done in leaf. Programmed identification of plant ailment helps in observing the yield and the root cause of the diseases. The infections are mostly depending on the climatic conditions. But the predictions and identification of diseases do not expect that information for the classification and identification model. The researchers have used only the images as input to the model which requires lot of filter to process the picture, then division and extraction of highlights and characterize them on the diverse premise. It utilizes numerous order systems, for example, k-means classifier (K-means), support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor (K-NN) classifier, fuzzy logic (FL), and recurrent neural network (RNN). Our paper identifies the ways of plant leaf detection and classification of diseases.