A Novel Approach Based on Convolutional Neural Networks for Maize Disease Detection

Abstract

This study presents an improved method for detecting and classifying diseases in corn leaves using an enhanced ResNet-18 Convolutional Neural Network (CNN) model. To address the challenge of limited data and enhance the model’s adaptability to real-world conditions, data augmentation techniques that simulate field environments are applied. The model is trained and tested on a curated dataset of maize leaf images, and its performance is evaluated using key metrics such as accuracy, precision, recall, and F1-score. The proposed approach achieves an excellent overall accuracy of 99.16%, significantly outperforming traditional CNN models. This demonstrates its strong performance and reliability, offering a scalable solution for automated plant disease detection, particularly in resource-constrained agricultural settings.