Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage
This framework proves to be a powerful tool for quantifying rice developmental dynamics and accurately predicting yield using readily available RGB imagery. It holds significant potential for advancing both precision agriculture and crop breeding efforts.
Authors
- Woo-Jin Im
- Sungyul Chang (ORCID: https://orcid.org/0000-0002-9439-608X)
- Ji‐Hyeon Lee (ORCID: https://orcid.org/0009-0009-5857-3139)
- Dongwon Kwon
- Wan-Gyu Sang (ORCID: https://orcid.org/0009-0007-2132-859X)
- Jae-Ki Chang
- Hyeok-Jin Bak
- Eunji Kim (ORCID: https://orcid.org/0000-0001-7986-8141)
- Nam-Jin Chung (ORCID: https://orcid.org/0000-0002-0369-7275)
- Woon-Ha Hwang (ORCID: https://orcid.org/0000-0002-4493-2446)
Institutions
- Rural Development Administration (KR)
- Jeonbuk National University (KR)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-08-14
- DOI
- https://doi.org/10.3389/fpls.2025.1611653
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Rural Development Administration
- Jeonbuk National University