Deep Learning Based CNN Model for Classification and Prediction of Leaf Diseases

Plants provide an enormous range of living beings with food. The growing population means that maintaining this supply is becoming more and more crucial. To deal with this, the plants need to be protected against several diseases. Consequently, it is critical to identify these diseases or illnesses as soon as feasible. The most current advances in deep learning and image processing methods should help farmers identify and categorize leaf diseases more quickly. This study suggests a paradigm for using convolutional neural networks (CNNs) to categorize leaf diseases. The CNN model is built for prediction of leaf disease for apple and tomato. The model is trained utilizing a batch size of 64 & 50 epochs. Using the CNN model, the recommended model achieved 98.88% accuracy. The prediction model shows better performance when tested with the sample data and it shows promising result.