Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities

TL;DR

This paper reviews plant disease datasets used in deep learning, proposing a taxonomy to differentiate and select datasets, and discusses future directions for creating challenge-oriented datasets and deploying real-world applications.

Contribution

It introduces an informative taxonomy for plant disease datasets and provides guidance for dataset selection and future dataset creation in deep learning applications.

Findings

Summarizes existing public RGB plant disease datasets.

Proposes directions for creating challenge-oriented datasets.

Highlights importance of deploying deep learning in real-world scenarios.

Abstract

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.