LeafNet: Design and Evaluation of a Deep CNN Model for Recognition of Diseases in Plant Leaves

Leaf disease prediction is an important problem in agriculture because it impacts crop yield and quality. It is feasible to reliably predict leaf detection uses in machine learning and image processing algorithms, which can avoid significant crop losses. This chapter suggests a method for predicting leaf disease that uses convolutional neural networks (CNNs) to extract features from leaf images and categorize them. The suggested method detected and classified different leaf diseases with high accuracy, making it a promising tool for early disease detection and prevention in agriculture. Here, we have used PlantVillage dataset to train our model. In the world of plant farming, it is vital to make sure that plants grow well and stay safe from diseases to get good crops. Diseases can harm plants, like making their leaves or stems sick or even killing them. Nowadays, we use fancy computer technology and smart learning programs to deal with plant problems. In this study, we made a smart computer system that looks at pictures of sick plant leaves and figures out what is wrong. It is really good at this job, better than other ways people have tried.