Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks
Results indicated that the model and image processing pipeline can serve as an alternative to human scoring. Eliminating human subjectivity for the scoring protocols will allow non-experts to collect scores and may enable drone-based data collection. This could reduce the time needed to obtain new lines or identify new genes responsible for leaf spot resistance in peanut.
Authors
- Michael W. Kudenov (ORCID: https://orcid.org/0000-0001-5601-6309)
- Robert Austin (ORCID: https://orcid.org/0000-0001-6096-8361)
- Jeffrey C. Dunne (ORCID: https://orcid.org/0000-0003-0544-9889)
- Cassondra Newman
- Joshua Larsen
Institutions
- North Carolina State University (US)
- North Central State College (US)
Publication Details
- Journal
- Plant Methods
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1186/s13007-024-01316-x
- Citations
- 1
- Primary Topic
- Peanut Plant Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.75
Funders
- National Institute of Food and Agriculture