Claim · model-written summary
Adding texture features to VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.801.
- Treatment
- texture features (TFs) incorporatione2
- Outcome
- R2e2
- Value
- 0.801e2
- Qualifier
- partial least squares regression (PLSR) combined with RF, SVM and GBDTe0
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleComparatorSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI”
e2“After texture features (TFs) incorporation, the R2 increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492”
t0“Estimating Maize Leaf Area Index Using Multi-Source Features Derived from UAV Multispectral Imagery and Machine Learning Models”
Title
Hongyan Li; Caixia Huang; Yuze Zhang; et al. Plants 2025. doi:10.3390/plants14223534
Other claims from this paper (18)
- Method / resource Using only VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.653.
- Method / resource Using only VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.697.
- Method / resource Using only VIs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.634.
- Method / resource Using only VIs, PLSR combined with RF gave an RMSE of 0.650 for maize LAI.
- Method / resource Using only VIs, PLSR combined with SVM gave an RMSE of 0.608 for maize LAI.
- Method / resource Using only VIs, PLSR combined with GBDT gave an RMSE of 0.668 for maize LAI.
- Method / resource Adding texture features to VIs, PLSR combined with RF estimated maize LAI with an R2 of 0.717.
- Method / resource Adding texture features to VIs, PLSR combined with SVM estimated maize LAI with an R2 of 0.794.
- Method / resource Adding texture features to VIs, PLSR combined with RF gave an RMSE of 0.587 for maize LAI.
- Method / resource Adding texture features to VIs, PLSR combined with SVM gave an RMSE of 0.500 for maize LAI.
- Method / resource Adding texture features to VIs, PLSR combined with GBDT gave an RMSE of 0.492 for maize LAI.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF estimated maize LAI with an R2 of 0.789.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM estimated maize LAI with an R2 of 0.804.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT estimated maize LAI with an R2 of 0.844.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with RF gave an RMSE of 0.506 for maize LAI.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with SVM gave an RMSE of 0.489 for maize LAI.
- Method / resource Adding texture indices to VIs and TFs, PLSR combined with GBDT gave an RMSE of 0.436 for maize LAI.
- Method / resource The multi-model fusion framework PLSR+GBDT with VIs, TFs and TIs estimated maize LAI with an R2 of 0.859 and 0.794 in independent test sets under contrasting conditions.