The identification and classification of plant diseases is of great significance to ecological protection and deep learning methods have made a great of progress in the common plant diseases identification for specific plant. While faced with the same plant disease of other plants, due to the insufficient or low quality training data, current deep learning methods will be difficult to identify the diseases effectively and accurately. Inspired by the advantages of GAN in dataset expansion, we propose the CycleGAN based confusion model in this paper. In this paper, GAN framework is improved by adding noise label and learn together during training stage, which migrates the data of common plant diseases to the plants with insufficient or low quality data. In order to evaluate the quality of the migrated training dataset among different GAN approaches, we introduce the quality indicators of the migration images such as MMD, FID, EMD etc. We compare our model with other GANs model, and the experimental results show that the proposed model obtains better results in the migration process, which make it more effective for the identification of cross species plant diseases.

中文翻译：

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基于 CycleGAN 的跨物种植物病害图像迁移混淆模型

植物病害的识别和分类对生态保护具有重要意义，深度学习方法在针对特定植物的常见植物病害识别方面取得了很大进展。而面对与其他植物相同的病害，由于训练数据不足或质量低下，目前的深度学习方法将难以有效准确地识别病害。受 GAN 在数据集扩展方面的优势的启发，我们在本文中提出了基于 CycleGAN 的混淆模型。在本文中，GAN 框架通过在训练阶段添加噪声标签和共同学习来改进，将常见植物病害的数据迁移到数据不足或质量低的植物中。为了评估不同 GAN 方法之间迁移的训练数据集的质量，