Maize Leaf Disease Detection and Classification Using CNN

This work proposes a deep learning approach for maize leaf disease detection and classification with optimized techniques in Convolutional Neural Networks. The adopted dataset includes images of diverse maize leaf diseases categorized into four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy, comprising 1,200 training samples, 400 validation samples, and 398 testing samples. The architecture of the model is designed to have convolutional layers, pooling layers, and dense layers for extracting features, while final classification is done with a softmax activation function. Advanced optimization techniques such as Adam optimizer, early stopping, and data augmentation were used to have the best generalization performance. Evaluation metrics include accuracy, loss, precision, recall, F1-score, and ROC-AUC, achieving an overall test accuracy of 93% with an AUC score of 0.99. The results indicate a robust solution for the accurate and scalable diagnosis of maize disease, with possible applications in agricultural monitoring systems.