Tomato Leaf Disease Detection and Classification Using Deep Learning

The biggest issue affecting food security is plant infections and diseases. As a result, one of the most important steps in the prevention and management of disease crop is the early detection of disease symptoms in the leaf portions. Prediction plant disease may avoid plant degradation, waste of energy and economic losses. Lowering the risk of worldwide food scarcity. The tomato is one of the main crops that is grown in huge quantities and has a significant commercial value. In this work, we suggest using CNN, a convolutional neural network-based deep learning architecture. Attained 97% accuracy on a dataset of 3716 tomato leaf photos from PlantVillage, which was used for training and testing to distinguish between tomato trees with healthy and diseased leaves. Our approach demonstrated great performance on a variety of criteria and can be used to agriculture to accurately and quickly identify plant health.