Image Data Augmentation for Plant Leaf Disease Classification Using Neural Style Transfer
In this paper, we investigate the effectiveness of various data augmentation methods in improving the generalization performance of the downstream plant disease classifiers. In particular, we propose the use of Neural Style Transfer (NST) to generate images of diseased plants from healthy plants as a cross-label data augmentation method for improving the performance of plant disease diagnosis in addition to applying conventional data augmentation techniques in order to improve the dataset class imbalance.