Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications

Abstract

An enhancement generator model with a progressive Wasserstein generative adversarial network and gradient penalized (PWGAN-GP) is proposed to solve the problem of low recognition accuracy caused by the lack of rice disease image samples in training CNNs. First, the generator model uses the progressive training method to improve the resolution of the generated samples step by step to reduce the difficulty of training. Second, to measure the similarity distance accurately between samples, a loss function is added to the discriminator that makes the generated samples more stable and realistic. Finally, the enhanced image datasets of three rice diseases are used for the training and testing of typical CNN models. The experimental results show that the proposed PWGAN-GP has the lowest FID score of 67.12 compared with WGAN, DCGAN, and WGAN-GP. In training VGG-16, GoogLeNet, and ResNet-50 with PWGAN-GP using generated samples, the accuracy increased by 10.44%, 12.38%, and 13.19%, respectively. PWGAN-GP increased by 4.29%, 4.61%, and 3.96%, respectively, for three CNN models over the traditional image data augmentation (TIDA) method. Through comparative analysis, the best model for identifying rice disease is ResNet-50 with PWGAN-GP in X2 enhancement intensity, and the average accuracy achieved was 98.14%. These results proved that the PWGAN-GP method could effectively improve the classification ability of CNNs.