PLANT DISEASE CLASSIFICATION USINE MACHINE LEARNING

Abstract

:  In India, a developing nation where agriculture supports nearly 58% of the rural population, tomatoes are a major crop. To prevent significant losses in tomato quantity and yield, it is essential to accurately identify and classify tomato plant diseases. Advanced technologies like image processing can be used to address this challenge using various techniques and algorithms. ASE is often leaf damage. This project uses four sequential stages to identify the specific type of disease: preprocessing, leaf segmentation, feature extraction, and classification. Preprocessing is used to remove noise from the images, while image segmentation isolates the affected areas of the leaflings, a guided, supervised, and advanced machine learning algorithm called k-nearest neighbours (KNN) is used. In the final stage, the user receives treatment recommendations. Diseases can have a devastating impact on living plants, highlighting the importance of early detection. This paper presents a novel approach to leaf disease detection using image processing, enabling farmers to identify tomato plant problems from images based on colour, boundaries, and texture. The goal is to provide farmers with timely and reliable results.