Claim · model-written summary
The vegetation index combining red edge and near-infrared bands was significantly correlated with LAI and LDM in wheat.
- Taxon
- wheat
Triticum aestivume0 - Treatment
- vegetation index combined with the red edge band and the near-infrared bande1
- Outcome
- LAI and LDMe1
- Setting
- three regions of Jiangsu Provincee0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleComparatorDirectionValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019”
e1“The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM”
Title
Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle
Zhaopeng Fu; Jie Jiang; Yang Gao; et al. Remote Sensing 2020. doi:10.3390/rs12030508
Other claims from this paper (4)
- Method / resource Machine learning methods (PLSR, ANN, RF) performed better than simple linear regression for predicting wheat yield.
- Method / resource The RF model using NDVI at four growth stages was the optimal wheat yield estimation model, with an R2 of 0.78.
- Method / resource The optimal wheat yield estimation model had a relative root mean square error (RRMSE) of 0.1030.
- Recommendation A multi-spectral camera on a multi-rotor UAV has a broad application prospect for wheat growth index monitoring and yield estimation.