{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:03Z","timestamp":1787029503181,"version":"build-2736575974"},"reference-count":75,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GDAS\u2019 Project of Science and Technology Development","award":["2018GDASCX-0101"],"award-info":[{"award-number":["2018GDASCX-0101"]}]},{"DOI":"10.13039\/100004952","name":"Minnesota Department of Agriculture","doi-asserted-by":"publisher","award":["AFREC R2019-20, R2020-32, R2021-32"],"award-info":[{"award-number":["AFREC R2019-20, R2020-32, R2021-32"]}],"id":[{"id":"10.13039\/100004952","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Minnesota Robotics Institute Seed Grant","award":["MNR-12"],"award-info":[{"award-number":["MNR-12"]}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["State project 1016571"],"award-info":[{"award-number":["State project 1016571"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74\u20130.90 for prediction) or random forest regression models (R2 = 0.84\u20130.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 &lt; 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.<\/jats:p>","DOI":"10.3390\/rs14020394","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Dan","family":"Li","sequence":"first","affiliation":[{"name":"Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"},{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8419-6511","authenticated-orcid":false,"given":"Yuxin","family":"Miao","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Curtis J.","family":"Ransom","sequence":"additional","affiliation":[{"name":"USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory Mac","family":"Bean","sequence":"additional","affiliation":[{"name":"McCain Foods, Oakbrook Terrace, IL 60181, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2268-9016","authenticated-orcid":false,"given":"Newell R.","family":"Kitchen","sequence":"additional","affiliation":[{"name":"USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9539-0050","authenticated-orcid":false,"given":"Fabi\u00e1n G.","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John E.","family":"Sawyer","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Iowa State University, Ames, IA 50011, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-854X","authenticated-orcid":false,"given":"James J.","family":"Camberato","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul R.","family":"Carter","sequence":"additional","affiliation":[{"name":"Corteva Agriscience, Johnston, IA 50131, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard B.","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Department of Agonomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W.","family":"Franzen","sequence":"additional","affiliation":[{"name":"School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carrie A. M.","family":"Laboski","sequence":"additional","affiliation":[{"name":"Department of Soil Science, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emerson D.","family":"Nafziger","sequence":"additional","affiliation":[{"name":"Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John F.","family":"Shanahan","sequence":"additional","affiliation":[{"name":"Agoro Carbon Alliance, 6807 Ridge Rd., Lincoln, NE 68512, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/nature15743","article-title":"Managing nitrogen for sustainable development","volume":"528","author":"Zhang","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.fcr.2017.09.033","article-title":"Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system","volume":"214","author":"Cao","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/s41586-019-1001-1","article-title":"Managing nitrogen to restore water quality in China","volume":"567","author":"Yu","year":"2019","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.2136\/sssaj2005.0396","article-title":"Potential impact of precision nitrogen management on corn yield, protein content, and test weight","volume":"71","author":"Miao","year":"2007","journal-title":"Soil Sci. 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