Proceedings of the Korea Contents Association Conference (한국콘텐츠학회:학술대회논문집)

The Korea Contents Association (한국콘텐츠학회)
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Synthetic Data Augmentation for Plant Disease Image Generation using GAN

GAN을 이용한 식물 병해 이미지 합성 데이터 증강

  • Nazki, Haseeb (Department of Electronics Engineering, Chonbuk National University) ;
  • Lee, Jaehwan (Department of Electronics Engineering, Chonbuk National University) ;
  • Yoon, Sook (Research Institute of Realistic Media and Technology, Department of Computer Engineering, Mokpo National University) ;
  • Park, Dong Sun (IT Convergence Research Centre, Chonbuk National University)
  • Published : 2018.05.11
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Abstract

In this paper, we present a data augmentation method that generates synthetic plant disease images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation techniques to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of 2789 images of tomato plant diseases (Gray mold, Canker, Leaf mold, Plague, Leaf miner, Whitefly etc.).

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