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

arXiv: https://arxiv.org/abs/2312.07905 · HTML: https://mlsanity.com/paper/2312.07905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 71 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [iCassava 2019 Fine-Grained Visual Categorization Challenge](https://mlsanity.com/paper/1908.02900/) (2019)
- [A Holistic Assessment of the Reliability of Machine Learning Systems](https://mlsanity.com/paper/2307.10586/) (2023)
- [Dhan-Shomadhan: A Dataset of Rice Leaf Disease Classification for Bangladeshi Local Rice](https://mlsanity.com/paper/2309.07515/) (2023)

## Abstract (arXiv)

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.