Leaf Disease Detection Using Deep Learning Approach

This study attempted to investigate leaf disease detection using deep learning. In present works, it was employed the Convolution neural network (CNN) to detect leaf disease. The proposed model involved four steps to determine the type of disease, which were preprocessing, feature extraction, CNN layer design, and classification, and thus, the proposed model detected the disease with reasonable accuracy. The dataset concerned more than thousands of images deployed for the training and evaluation of the model. The validation part was also made with 25% of the dataset, 70% for training, and 5% for model testing. The designed model in this study provided 81.25% accuracy.