ArticleFrontiers in plant science2023
Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Animal Venoms Targeting Cellular Mechanisms: Advances and Implications for Drug Discovery and Disease Therapy.Toxins · 2026Review
- PLDC-Net: A Domain-Specific Base Model for Plant Leaf Disease Classification Domain Adaptation Tasks.Plant direct · 2026Article
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- A lightweight and explainable CNN model for empowering plant disease diagnosis.Scientific reports · 2025Article
- Applications of transfer learning in sunflower disease detection: advances, challenges, and future directions.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Comprehensive plant health monitoring: expert-level assessment with spatio-temporal image data.Frontiers in plant science · 2025Article
- A multimodal framework for pepper diseases and pests detection.Scientific reports · 2024Article
- Plant disease recognition datasets in the age of deep learning: challenges and opportunities.Frontiers in plant science · 2024Article
- Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.Frontiers in plant science · 2023Article
- An iterative noisy annotation correction model for robust plant disease detection.Frontiers in plant science · 2023Article
- A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection.Frontiers in plant science · 2023Article
- Deep learning for medicinal plant species classification and recognition: a systematic review.Frontiers in plant science · 2023Review
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Authors and funding
8 authors.
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Abstract
Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the
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