MACHINE LEARNING FOR DETECTION AND PREDICTION OF TOMATO LEAF DISEASES

Abstract

Tomato, which is scientifically known as Solanum lycopersicum, is a widely cultivated plant in the nightshade family, Solanaceae. It is an important source of food, both fresh and in processed form, and is grown in many parts of the world. However, tomato plants are prone to various diseases, which can significantly reduce their yield and quality. Early detection and prediction of these diseases can help in timely treatment and management which can ultimately lead to higher crop productivity. Machine learning techniques have shown promise in detecting and predicting plant diseases. This approach can be used to improve the efficiency and effectiveness of tomato cultivation and can have a significant impact on the agricultural industry. The use of machine learning algorithms can increase the efficiency of tomato cultivation. In this study, we present a machine learning-based approach for the detection and prediction of tomato leaf diseases. We use a dataset of images of tomato leaves infected with different diseases such as tomato yellow curl virus,
bacterial spot, and late blight along with healthy leaves, to train a Random Forest model. The model is then tested on a separate dataset to evaluate its performance