Comparative Evaluation of CNN Architectures for Corn Leaf Diseases Classification

Corn has particular importance in the global food industry. Many diseases attack the corn crops, which affects the crop yield. Early classification and detection of these diseases are pivotal to preventing damage and achieving high crop productivity. Although deep learning, especially convolutional neural networks, has accomplished remarkable results in image recognition, selecting the optimal architecture and using limited datasets remains a challenge. To address this gap, a transfer learning approach based on ImageNet weights was applied to classify three common corn diseases (i.e., gray spot, common rust, and blight), as well as the healthy plants. Six CNN architectures—DenseNet201, EfficientNetB0, VGG16, ResNet50, InceptionV3, and InceptionResNetV2—inclusive performance was evaluated for classification on a corn dataset. Based on evaluation metrics, EfficientNetB0 achieves the highest training accuracy of 97.67% with a fast computational time of 71 seconds. It performs more efficiently than the other architectures. These findings support the use of deep learning models, particularly EfficientNet, in the evolution of artificial intelligence image classification system applications.