A Data Augmented Method for Plant Disease Leaf Image Recognition based on Enhanced GAN Model Network

Abstract

The identification of plant disease leaves based on deep learning is the key to control the development and spread of plant diseases. In this paper, the existing problems of traditional classification and recognition of plant disease leaves and the limitations of deep learning-based plant disease leaf training are analysed. An enhanced GAN model network based on the Wasserstein GAN loss function has been developed to address the limited training images of plant disease leaves. The self-attention layer is added into the self-encoding structure of the generating network. The effectiveness of data generated by the encoder is increased after the self-attention layer is added after the convolution. Finally, the model's training process is stabilised using the depth gradient punishment method. Three types of corn disease photos and 100 health images from the PlantVillage dataset were used as data sets in the experiment. An AWGAN model was applied to generate around 3000 images. Several data improvement techniques were applied to augment the same datasets. Comparative tests are conducted using AlexNet, VGG16, and ResNet18. The results indicate that the proposed AWGAN model is capable of generating sufficient images of maize leaf diseases with apparent lesions, making it a viable solution for data augmentation of plant disease images. The training model's recognition accuracy is significantly increased. The proposed awGAN-based image identification method for plant leaf disease efficiently resolves the over-fitting problem in the small sample training set. The model recognition accuracy in the ResNet18 network achieves 98.4%.