ArticleScientific data2026
A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Longitudinal multispectral image dataset for ToBRFV disease detection in tomato and pepper plants.Data in brief · 2026Article
- A Manually Annotated and Curated Mediterranean Plant Image Dataset of Native Species from Lebanon.Scientific data · 2026Article
- Optimized Lightweight U-Net and YOLACT framework for multi-disease severity detection in pome fruit leaves.Scientific reports · 2026Article
- A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.Scientific data · 2026Article
- ReLeaf-SAM: reliability-guided detail compensation for SAM-based plant disease segmentation.Frontiers in plant science · 2026Article
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Gradient-guided boundary-aware selective scanning with multi-scale context aggregation for plant lesion segmentation.Frontiers in plant science · 2025Article
- The pipelines of deep learning-based plant image processing.Quantitative plant biology · 2025Review
- Robust plant disease segmentation in complex field environments: an in-depth analysis and validation with STAR-Net.Frontiers in plant science · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Plant diseases pose significant threats to agriculture, making proper diagnosis and effective treatment crucial for protecting crop yields. In automatic diagnosis processing, image segmentation helps to identify and localize diseases. Developing robust image segmentation models for detecting plant diseases requires high-quality annotations. Unfortunately, existing datasets rarely include segmentation labels and are typically confined to controlled laboratory settings, which fail to capture the complexity of images taken in the wild. Motivated by these, we established a large-scale segmentation dataset for plant diseases, dubbed PlantSeg. In particular, PlantSeg is distinct from existing datasets in three key aspects: (1) Annotation types: PlantSeg includes detailed and high-quality disease area masks. (2) Image sources: PlantSeg primarily comprises in-the-wild plant disease images rather than laboratory images provided in existing datasets. (3) Scale: PlantSeg contains the largest number of in-the-wild plant disease images, including 7,774 diseased images with corresponding segmentation masks. This dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.