Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations. - Curtis J. Ransom; Newell R. Kitchen; James J. Camberato; Paul R. Carter; Richard B. Ferguson; Fabián G. Fernández; David W. Franzen; Carrie A. M. Laboski; D. Brenton Myers; Emerson D. Nafziger; John E. Sawyer; John F. Shanahan (Comput. Electron. Agric., 2019)

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. - Tyler J. Nigon; Ce Yang; Gabriel Dias Paiao; David J. Mulla; Joseph F. Knight; Fabián G. Fernández (Remote. Sens., 2020)

Estimation of corn yield based on hyperspectral imagery and convolutional neural network. - Wei Yang 022; Tyler J. Nigon; Ziyuan Hao; Gabriel Dias Paiao; Fabián G. Fernández; David J. Mulla; Ce Yang (Comput. Electron. Agric., 2021)

Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System. - Cadan Cummings; Yuxin Miao; Gabriel Dias Paiao; Shujiang Kang; Fabián G. Fernández (Remote. Sens., 2021)

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; et al. (Remote. Sens., 2022)

The Influence of Aerial Hyperspectral Image Processing Workflow on Nitrogen Uptake Prediction Accuracy in Maize. - Tyler J. Nigon; Gabriel Dias Paiao; David J. Mulla; Fabián G. Fernández; Ce Yang (Remote. Sens., 2022)