With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on CNN, but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through Grabcut. It shows high performance through image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images except for the actual input as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed higher performance than previous researches, and image-based scores showed the best performanc

翻译：随着智慧农业的近期发展，研究人员对此领域表现出浓厚兴趣，其中病害诊断尤为重要。病害诊断属于异常检测领域，旨在区分植物或果实是否处于正常状态。该问题可通过基于CNN的二分类或多分类方法解决，亦可借助图像重建技术实现。然而受限于图像生成性能，当前最先进方法提出利用潜向量误差的分数计算方法。本文提出一种针对辣椒的专用网络，通过GrabCut算法去除背景，并基于图像分数计算方法展现出优异性能。由于输入图像重建的困难性，输入输出图像间差异较大。但本文提出的串行自编码器摒弃实际输入，仅利用两幅生成图像间的差异作为评分依据。我们提出利用GAN结构生成有意义图像的方法，并通过单一判别器同时对三个结果进行分类。实验表明，本方法性能优于现有研究，且基于图像的评分方式取得了最佳效果。