Life Sciences Commons^™

Corn Nitrogen Nutrition Index Prediction Improved By Integrating Genetic, Environmental, And Management Factors With Active Canopy Sensing Using Machine Learning, 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

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

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 was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen …