Prediction of Disease in Tomato Leaves with use of Machine Learning Technique

Abstract

India's sizable agricultural market provides the perfect conditions for cultivating a variety of products, including the tomato harvest. Detecting the transmission of diseases from unhealthy to healthy plants poses a severe threat to the agricultural industry because, if caught early enough, they can quickly spread and perhaps infest the entire farm. In terms of profit in good forming, early stage crop disease identification and severity monitoring is quite important. K-means clustering with fuzzy logic is used in the proposed study to evaluate the disease-affected region of the leaf and, as a result, assess the severity of the diseases. In this thesis, illnesses are detected using machine learning models for convolutional neural networks (CNN) and K-nearest neighbours (KNN).