Claim · model-written summary
At Feekes 11, transfer learning reached R² 0.82 versus baseline near zero.
- Taxon
- winter wheat
Triticum aestivumt0 - Organ / stage
- 11e0
- Outcome
- R 2e0
- Value
- 0.82e0
- Qualifier
- near 0e0
- Comparator
- baseline modele0
Not reportedMaterialGeneCompoundInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“at Feekes stages 10.5 and 11, where DTL achieved an R 2 of 0.78 and 0.82, respectively, compared to baseline model R 2 values near 0”
t0“winter wheat across temporal and spatial variability”
Title
Swas Kaushal; Mohammad Maruf Billah; Mandeep Singh; et al. The Plant Phenome Journal 2026. doi:10.1002/ppj2.70085
Other claims from this paper (6)
- Method / resource Cross-year baseline prediction had R² −2.9 in 2023.
- Method / resource With 20% fine tuning, cross-year prediction improved to R² 0.83.
- Method / resource Cross-location baseline R² ranged from −15.3 to −0.4.
- Method / resource With 40% fine tuning, cross-location R² improved to 0.29–0.69.
- Method / resource At Feekes 10.5, transfer learning reached R² 0.78 versus baseline near zero.
- Method / resource The authors conclude transfer learning improves accuracy and offers a resource efficient breeding tool.