ANALYSIS AND DESIGN OF CLASSIFICATION MODELS FOR PLANT DISEASES USING MACHINE LEARNING APPROACH

Abstract

In agriculture, there are several research topics targeted at increasing production and quality while reducing costs and increasing profits. Plant development is influenced by a variety of factors, the most common of which being plant diseases. The first step in treating leaf illnesses would be to identify them. Plant leaf disease diagnosis is a field of study that examines a plant's leaf to determine whether or not it has a disease. Tracking the health and illness of a plant is extremely important to a farm's effective crop production. The aforementioned numbers highlight the importance of early and effective disease identification in these plants, since failure or delay can result in large losses. Many studies have recently been conducted in order to address these difficulties, and this is currently a hotspot of research in the agricultural world. Paddy is the world's most important cereal crop. It is a main food for more than 50% of globe's population. Biotic and Abiotic elements such as virus, bacteria, pests, temperature, soil fertility, and precipitation influenced rice yields. Farm owners invest excessive energy and time on management of the diseases, and they guess the diseases with manual observation. The progress of technical assistance in farming has made it much easier to identify pathogens in paddy leaves automatically.