Claim · model-written summary
For cotton yield, MHRE substantially improved prediction (R2 = 0.82, RMSE = 0.33 t ha−1) over the best individual model RF (R2 = 0.77, RMSE = 0.36 t ha−1).
- Taxon
- cotton
Gossypium hirsutume1 - Outcome
- yield trait predictione1
- Direction
- substantially improvede1
- Value
- R2 = 0.82, RMSE = 0.33 t ha−1e1
- Qualifier
- RF: R2 = 0.77, RMSE = 0.36 t ha−1e1
- Comparator
- best individual modele1
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSetting
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“for cotton, MHRE substantially improved yield trait prediction (R2 = 0.82, RMSE = 0.33 t ha−1) compared to the best individual model (RF: R2 = 0.77, RMSE = 0.36 t ha−1)”
Title
Yu Qin; Moughal Tauqir; Xiang Yu; et al. Sensors 2026. doi:10.3390/s26020375
Other claims from this paper (7)
- Method / resource The MHRE ensemble performed best for rice and cotton and significantly outperformed the individual models RF, XGBoost, CatBoost and LightGBM.
- Method / resource For rice yield, MHRE reached the highest accuracy (R2 = 0.78, RMSE = 0.59 t ha−1) versus the best individual model XGBoost (R2 = 0.76, RMSE = 0.61 t ha−1).
- Method / resource Effective spike number in rice also showed strong predictability (R2 = 0.64, RMSE = 27.81 10,000·spike ha−1).
- Method / resource For cotton, accuracy for bolls per plant was the highest of the traits (R2 = 0.93, RMSE = 2.27 bolls plant−1).
- Method / resource The crop-specific MHRE model for rice was robust across five rice varietal groups.
- Method / resource The crop-specific MHRE model for cotton was robust across three cotton varietal groups.
- Method / resource The crop-specific MHRE models were applicable across six distinct regions in China.