An Improved EigenGAN-based Method for Data Augmentation for Plant Disease Classification

Plant diseases are caused by a variety of environmental variables, which cause large losses in productivity so the diagnostic systems that are automated play a significant part in agricultural automation.A large number of disease images with appropriate plant village database disease label information must be collected to construct a functional image-based autonomous image diagnostic system.However, manual detection of plant diseases is a time-consuming and error-prone process.Conventional systems showed reasonably good diagnostic performance, however, most of their disease predictions were heavily unfairness owing to "latent similarity" within a dataset (backgrounds, lighting, and/or the separation between the target and the camera) among training and test images, and their genuine diagnosis skills were far lower than stated.To overcome this issue, this paper proposed a Hybrid Fourier Filter De-noising (HFFDF) algorithm and enhanced EigenGAN (Generative Adversarial Network (GAN)), which creates a large number of diverse and large-quality training images and serves as a reliable data supplement for the diagnostic classifier.These produced images may be utilized as resources to improve the efficiency of plant disease diagnostic systems.The results shown that the performance of the new method of HFFDF is effective compared with other denoising filters of Gaussian, Median and wiener filter algorithms.The Experimental result shows that proposed HFFDF and EigenGAN methods clearly outperforms than existing methods.