Li, D.; Miao, Y.; Ransom, C.J.; Bean, G.M.; Kitchen, N.R.; Fernández, F.G.; Sawyer, J.E.; Camberato, J.J.; Carter, P.R.; Ferguson, R.B.;
et al. Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning. Remote Sens. 2022, 14, 394.
https://doi.org/10.3390/rs14020394
AMA Style
Li D, Miao Y, Ransom CJ, Bean GM, Kitchen NR, Fernández FG, Sawyer JE, Camberato JJ, Carter PR, Ferguson RB,
et al. Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning. Remote Sensing. 2022; 14(2):394.
https://doi.org/10.3390/rs14020394
Chicago/Turabian Style
Li, Dan, Yuxin Miao, Curtis J. Ransom, Gregory Mac Bean, Newell R. Kitchen, Fabián G. Fernández, John E. Sawyer, James J. Camberato, Paul R. Carter, Richard B. Ferguson,
and et al. 2022. "Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning" Remote Sensing 14, no. 2: 394.
https://doi.org/10.3390/rs14020394
APA Style
Li, D., Miao, Y., Ransom, C. J., Bean, G. M., Kitchen, N. R., Fernández, F. G., Sawyer, J. E., Camberato, J. J., Carter, P. R., Ferguson, R. B., Franzen, D. W., Laboski, C. A. M., Nafziger, E. D., & Shanahan, J. F.
(2022). Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning. Remote Sensing, 14(2), 394.
https://doi.org/10.3390/rs14020394
