LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis

@article{Cap2020LeafGANAE,
  title={LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis},
  author={Q. H. Cap and H. Uga and S. Kagiwada and H. Iyatomi},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.10100}
}
Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test datasets from new environments. The typical reasons for this are that the symptoms to be detected are unclear or faint, and there are limitations related to data diversity. In this paper, we propose LeafGAN, a novel image-to-image… Expand

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