GACN: Generative Adversarial Classified Network for Balancing Plant Disease Dataset and Plant Disease Recognition.

Abstract

Plant diseases are a critical threat to the agricultural sector. Therefore, accurate plant disease classification is important. In recent years, some researchers have used synthetic images of GAN to enhance plant disease recognition accuracy. In this paper, we propose a generative adversarial classified network (GACN) to further improve plant disease recognition accuracy. The GACN comprises a generator, discriminator, and classifier. The proposed model can not only enhance convolutional neural network performance by generating synthetic images to balance plant disease datasets but the GACN classifier can also be directly applied to plant disease recognition tasks. Experimental results on the PlantVillage and AI Challenger 2018 datasets show that the contribution of the proposed method to improve the discriminability of the convolution neural network is greater than that of the label-conditional methods of CGAN, ACGAN, BAGAN, and MFC-GAN. The accuracy of the trained classifier for plant disease recognition is also better than that of the plant disease recognition models studied on public plant disease datasets. In addition, we conducted several experiments to observe the effects of different numbers and resolutions of synthetic images on the discriminability of convolutional neural network.

Plant phenotyping relevance

植物病害画像から病害状態を認識するGACNを開発し、複数データセットで性能比較・検証しており、病害表現型の取得・推定手法が中心である。

Code and data availability

The supplied blocks describe a GAN-based plant disease recognition method evaluated on the public PlantVillage and AI Challenger 2018 datasets, but contain no author code release, trained model checkpoint, generated synthetic image dataset, or data availability statement. The only datasets mentioned are pre-existing公共c

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