Tomato_Leaf_ Disease

his project focuses on building a machine learning model to classify tomato leaf diseases using image processing and the XGBoost algorithm. The dataset contains images of tomato leaves belonging to different classes such as bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, target spot, two-spotted spider mite, yellow leaf curl virus, and healthy leaves. The images are preprocessed by resizing and converting them into numerical features. The dataset is then split into training and testing sets, and an XGBoost classifier is trained to recognize patterns associated with each disease. Finally, the model can predict the disease of a tomato leaf when a new image is provided.