Automatic Maize Leaf Disease Recognition Using Deep Learning

Abstract

Maize leaf diseases exhibit visible symptoms and are currently diagnosed by expert pathologists through personal observation, but the slow manual detection methods and pathologist's skill influence make it challenging to identify diseases in maize leaves. Therefore, computer-aided diagnostic systems offer a promising solution for disease detection issues. While traditional machine learning methods require perfect manual feature extraction for image classification, deep learning networks extract image features autonomously and function without pre-processing. This study proposes using the EfficientNet deep learning model for the classification of maize leaf diseases and compares it with another established deep learning model. The maize leaf disease dataset was used to train all models, with 4188 images for the original dataset and 6176 images for the augmented dataset. The EfficientNet B6 model achieved 98.10% accuracy on the original dataset, while the EfficientNet B3 model achieved the highest accuracy of 99.66% on the augmented dataset.