## 5 Conclusions  
In this study, machine learning regression methods were tested to predict wheat CNW using UAV MicaSense band reflectances, PlanetScope band reflectances, associated VIs, plant height, LAI, soil moisture, and topographic metrics. For UAV models using 28 variables, the combination of 12, 20, and 27 May data with the RF validation model produced the best results with an R\({}^{2}\) of 0.74 and an RMSE of 2.76 g/m\({}^{2}\). From the model's variable importance plot, the top 7, 8, 9, 13, 16, and spectral-only variable groups were tested. The best validation model used SVR with the top seven variables, which included plant height, LAI, four VIs, and the MicaSense NIR band. For the PlanetScope models using 24 variables, the best performing model was RF with 12, 20, and 27 May data resulting in an R\({}^{2}\) of 0.83 and an RMSE of 1.77 g/m\({}^{2}\). Based on the model's variable importance plot, the top 6, 10, 13, 17, and spectral-only variable groups were tested. The validation model with the best performance was RF using the top 17 variables including height, LAI, all four PlanetScope bands, and 11 VIs.  
A common limitation of in-situ agricultural models, including those developed in this study, are their empirical nature, and applicability can be limited to the dataset they are built and validated upon. Each field and growing season has different conditions and factors that affect plant growth, so models will need further testing to determine their effectiveness in precision agriculture methods. PlanetScope satellite constellation has also launched, and a third generation of sensors were introduced in 2020, known as SuperDove, with the potential to capture imagery with eight spectral bands including a red-edge band. Future work can consider further testing satellite-based nitrogen prediction models including red-edge VI variables.  
**Author Contributions:** Conceptualization, J.Y., J.W. and B.L.; methodology, J.Y., J.W. and B.L.; software, J.Y. and Y.S.; validation, J.W. and B.L.; formal analysis, J.Y.; investigation, J.Y.; resources, J.W. and B.L.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y., J.W. and B.L.; visualization, J.Y.; supervision, J.W. and B.L.; project administration, B.L.; funding acquisition, B.L. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the NSERC CRD, grant number CRDPJ507141-16, awarded to [PERSON] and [PERSON], and is the result of a collaboration between the University of New Brunswick, University of Western Ontario, and A&L Canada Laboratories Inc. Additional funding was provided by the NSERC discovery grant awarded to [PERSON] and the Ontario Graduate Scholarship awarded to [PERSON].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data collected for this paper are not publicly available.  
**Acknowledgments:** The authors thank A&L Canada Laboratories Inc. staff and [PERSON]'s GITA lab members for their assistance with data collection, lab processing, and support. Special thanks go to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in fieldwork and guidance. The authors would also like to thank the anonymous reviewers for their helpful feedback for this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.