Improving Oriental Melon Leaf Disease Classification via DCGAN-Based Image Augmentation

Abstract

Deep learning-based plant disease classification models often suffer from performance degradation when training data are limited. Hence, generative models offer a promising solution for model performance in plant disease classification. In this work, images representing powdery mildew, downy mildew, and healthy plant leaves were generated using traditional augmentation methods as well as both DCGAN and a modified DCGAN featuring residual connection blocks with varied activation functions. Evaluation metrics IS and FID revealed that the modified DCGAN consistently produced generative images with strong class-distinctive features and greater overall diversity compared to basic GAN methods, with an IS increment of 7.9% to 11.54% and FID decrement of 6.6% to 7.8%. After selecting the best augmentation method, we input the generated images into the training sets for the classification models, AlexNet, VGG16, and Goog-LeNet, to measure improvements in disease recognition. All classifiers benefited from the augmented datasets, with the modified DCGAN-based augmentation yielding the highest precision, recall, and accuracy. GoogLeNet outperformed all classification models, with an overall precision, recall, and F1-Score value of 98%. Notably, this generative approach minimized errors between visually similar categories, such as powdery mildew and healthy samples, by capturing subtle morphological differences. The results confirm that class-aware generative augmentation can both expand the number of training images and preserve the critical features necessary for discrimination, significantly boosting model effectiveness. These advances show the practical potential of generative models not only to enrich datasets but also to improve the accuracy and robustness of plant disease detection for real-world agricultural scenarios.