Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study wa...

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Bibliografiska uppgifter

Huvudupphov:	Dan Li, 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, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John F. Shanahan
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2022-01-01
Serie:	Remote Sensing
Ämnen:	nitrogen status diagnosis machine learning normalized difference red edge (NDRE) data fusion
Länkar:	https://www.mdpi.com/2072-4292/14/2/394
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