ArticleFrontiers in plant science2024
Plant disease recognition datasets in the age of deep learning: challenges and opportunities.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Tomato leaf disease and severity prediction using multi-task learning.BMC plant biology · 2026Article
- PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks.Plant direct · 2026Article
- ZDAM: a new deep learning model for bean leaf disease diagnosis.Frontiers in plant science · 2026Article
- A comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications.Data in brief · 2025Article
- Enhancing anomaly detection in plant disease recognition with knowledge ensemble.Frontiers in plant science · 2025Article
- A review of plant leaf disease identification by deep learning algorithms.Frontiers in plant science · 2025Review
- Adaptive preprocessing and Cascaded Canny Edge Segmentation for cassava disease identification using HyperCapsInception-ResNet-V2-CNN.Frontiers in plant science · 2025Article
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5 authors.
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Abstract
Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets.
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