Leaf lesion images from Arabidopsis plants infected with Sclerotinia sclerotiorum, supporting the paper 'LIME: A Fully Automated Pipeline for High-Throughput Quantification of Leaf Lesions'. The dataset was authored by Tan, Da and is hosted on the Borealis Harvested Dataverse. It was last updated on 2026-04-25.
Use Cases
- Training computer vision models for leaf lesion segmentation based on the described high-throughput quantification pipeline.
- Benchmarking automated plant disease severity assessment tools against the LIME pipeline methodology.
- Studying the phenotypic response of Arabidopsis thaliana to Sclerotinia sclerotiorum infection.
- Developing image-based metrics for plant pathology research based on the described lesion quantification task.
Strengths
- Dataset is directly linked to a published research paper, providing methodological context.
- Last update timestamp is explicitly provided as 2026-04-25 04:10:56.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file size are unknown, which may limit suitability assessment.
Provenance
- Source
- Borealis Harvested Dataverse
- Collection Method
- Likely contains images generated from experimental infections of Arabidopsis plants with Sclerotinia sclerotiorum.
- Time Range
- null
- Freshness
- Last updated 2026-04-25 04:10:56; freshness should be verified.
- Geography
- Manitoba, Canada (implied from title)