Application of Data Enhancement Method Based on Generative Adversarial Networks for Soybean Leaf Disease Identification

Abstract

Soybean leaf disease data collection is an expensive and time-consuming task. Convolutional neural network training requires a large amount of data, but traditional data enhancement methods (such as rotation, flipping, translation) are restricted by fixed rules and cannot generate images with diversity and variability. Aiming at the problem of the lack of soybean leaf disease data set, this study proposes a data enhancement method based on Generative Adversarial Networks (GANs) for soybean leaf disease identification. The method is based on a cyclic adversarial network and its discriminator uses dense connections. Strategies to reduce the size and computational complexity of the final model. Using a cyclic adversarial network to convert between healthy and diseased leaves, unsupervised learning can be performed, using limited images to learn disease characteristics, there by generating highly recognizable soybean leaf images. Synthesis images generated from GANs and original images are fed together as the model training set input and the recognition model for recognizing 9 types of soybean leaf images is obtained. An accuracy rate of 95.89% can be achieved on the verification set. Experimental results show that the generative adversarial network provided in this article can: Generate soybean leaf disease image data with high discriminative features, increase the size of the data set and provide a feasible solution for soybean leaf disease image data enhancement; as A regularization strategy to reduce over-fitting problems and improve the performance of the recognition model.