PHTFNet-RPM: a probabilistic hybrid network with RPM for tobacco root disease forecasting
The results confirm that the model can reliably forecast disease progression trends under different configurations, even when relying solely on historical weather observations. The integration of uncertainty quantification provides a robust tool for assessing prediction reliability, offering significant practical value for disease management.
Authors
- Shengling Geng (ORCID: https://orcid.org/0000-0002-9897-3147)
- Yao TingShan (ORCID: https://orcid.org/0000-0003-2162-1626)
- Yuzhe Bu (ORCID: https://orcid.org/0000-0003-0706-910X)
- Renjie Huang (ORCID: https://orcid.org/0000-0002-9996-3354)
Institutions
- Southwest University (CN)
- Citrus Research Institute (CN)
- Chuxiong Normal University (CN)
Publication Details
- Journal
- Frontiers in Big Data
- Published
- 2025-11-10
- DOI
- https://doi.org/10.3389/fdata.2025.1705587
- Primary Topic
- Plant Disease Resistance and Genetics
- Type
- article
- Field-Weighted Citation Impact
- 0.00