Plant Disease Recognition Datasets in the Age of Deep Learning:
  Challenges and Opportunities
Mingle Xu Ji Eun Park Jaehwan Lee Jucheng Yang Sook Yoon
Plant disease recognition has witnessed a significant improvement with deep
learning in recent years. Although plant disease datasets are essential and
many relevant datasets are public available, two fundamental questions exist.
First, how to differentiate datasets and further choose suitable public
datasets for specific applications? Second, what kinds of characteristics of
datasets are desired to achieve promising performance in real-world
applications? To address the questions, this study explicitly propose an
informative taxonomy to describe potential plant disease datasets. We further
provide several directions for future, such as creating challenge-oriented
datasets and the ultimate objective deploying deep learning in real-world
applications with satisfactory performance. In addition, existing related
public RGB image datasets are summarized. We believe that this study will
contributing making better datasets and that this study will contribute beyond
plant disease recognition such as plant species recognition. To facilitate the
community, our project is public https://github.com/xml94/PPDRD with the
information of relevant public datasets.