Integrating meteorological and breeding data to predict maize yields using machine learning algorithms
These results demonstrate that the proposed RF-based model can provide accurate yield predictions for specific maize cultivars under diverse planting conditions. This predictive framework offers practical support for farmers in selecting well-adapted hybrids and serves as a cost-effective, efficient tool for breeders to identify high-yielding maize hybrids optimized for particular environments. Consequently, the model promotes smarter breeding strategies and more precise cultivation recommendations.
Authors
- Yanbing Wang (ORCID: https://orcid.org/0000-0002-8621-7360)
- Shaoqiang Wang (ORCID: https://orcid.org/0000-0002-4111-6689)
- Guangcai Wang (ORCID: https://orcid.org/0000-0003-3631-1428)
- Xuwen Jiang (ORCID: https://orcid.org/0000-0003-3537-9631)
- Jianwei Wei (ORCID: https://orcid.org/0000-0002-6872-3534)
- Huiguang Wang
- Yuchen Wang (ORCID: https://orcid.org/0000-0002-0408-7508)
- Weixian Li (ORCID: https://orcid.org/0000-0002-7741-176X)
Institutions
- Qingdao University of Science and Technology (CN)
- Qingdao Agricultural University (CN)
- Qingdao Academy of Agricultural Sciences (CN)
- Qingdao Municipal Center for Disease Control and Prevention (CN)
- Hebei Academy of Agriculture and Forestry Sciences (CN)
- Langfang Normal University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-21
- DOI
- https://doi.org/10.3389/fpls.2025.1722068
- Primary Topic
- Climate change impacts on agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- Swansea University