Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut
  and Generative Adversarial Serial Autoencoder
Jongwook Si Sungyoung Kim
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