Data · dataset · 2026
SWM Image Dataset
Listed in REDU - Unicamp Institutional Research Data Repository
Computer vision enables automated, accurate plant disease scouting, especially for diseases with patchy field distributions and lifecycles that begin in the soil and evolve across soil and plants.
Description
This is the case for the soilborne fungal pathogen Sclerotinia sclerotiorum, the causal agent of white mold in over 400 hosts, such as common bean, soybean, cotton, sunflower, canola, and tobacco, threatening sustainable agriculture on all continents.
There are image datasets focused on plant disease detection, including the Plant Disease Database (PDDB) and PlantDoc, which cover a large range of plant diseases. None of them considers real-field images of S. sclerotiorum and white mold for detection. The novel dataset fills this gap by providing images of common bean crops annotated by plant pathologists, covering three life cycle stages: dormant sclerotia, germinated apothecia, and white mold symptoms.
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The image set is structured into two datasets: the SWM Dataset and the R-SWM Dataset. The SWM Dataset consists of a preprocessed and class-balanced version, whereas the R-SWM Dataset preserves the original raw-field images. Both datasets provide comprehensive annotations in YOLO and Pascal VOC formats, facilitating their adoption across different object detection models.
The datasets are split into training, validation, and test sets, enabling the development of deep learning-based detection approaches to improve detector performance, address related challenges, and advance research in computer vision and plant pathology.
Links
Where it is published
- Dataverse dataset page redu.unicamp.br/dataset.xhtml?persistentId=doi%3A10.25824%2Fredu%2FNMIP43 ↗
landing page · from redu unicamp br
- DOI doi.org/10.25824/redu/nmip43 ↗
DOI / persistent id · from redu unicamp br
Catalogue records · 1
- Dataverse API redu.unicamp.br/api/datasets/:persistentId/?persistentId=doi%3A10.25824%2Fredu… ↗
metadata API · from redu unicamp br
Topics
- Stated by source
- Agricultural Sciences · Computer and Information Science
- From keywords
- Computer Science & AI · Deep learning · Life Sciences · Medicine & Health
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| REDU - Unicamp Institutional Research Data Repository | doi:10.25824/redu/NMIP43 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[disease].local:disease:disease | enrichment · redu unicamp br | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:461103 | mapping · redu unicamp br | vocabulary-mapper@1.0.0 | keywords['Deep learning'] |
| concepts[field].dataverse_subject:agricultural-sciences | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[field].dataverse_subject:computer-and-information-science | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · redu unicamp br | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| description | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /description |
| publication_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| title | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /name |
| updated_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| version_label | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 |