Deep Learning-Based CNN Approach for Tomato Leaf Disease Detection

In Bangladesh, more tomatoes have been grown in the last few years. In addition to its health benefits, tomato farming is important for the work of many people. However, several illnesses that affect tomato leaves hinder tomato output. The goal of my study is to use convolutional neural networks (CNNs). This study looks into how to find diseases on tomato leaves. My study shows that CNNs have the revolutionary ability to change the way farming is done, which goes beyond their statistical successes. Because these models can find diseases quickly and correctly, they hold a lot of promise for long-term crop management and a big boost in food security around the world. Three types of tomato leaf diseases were looked at in this study, along with one healthy type. To test, 10% of the examples in each class were taken out. Only 20% was used for evaluation, and the other 70% was used for teaching. I look into five different designs in great detail, looking at their speed, processing efficiency, and social effects. These are ResNet50, DenseNet201, MobileNetV2, MobileNetV3, and VGG19. The system showed that it was correct 95.37% of the time. It is regarded as an easy- to-use technology that will assist vegetable farmers, particularly those who cultivate "tomatoes," in reducing pest suppression by detecting leaf illnesses and increasing production by creating additional options for professional marketing and researching various vegetable diseases.