{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:45:21Z","timestamp":1769586321283,"version":"3.49.0"},"reference-count":84,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Colorado State University Agricultural Experiment Station in partnership with Force-A"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Characterizing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Practitioners of precision N management require determination of in-season plant N status in real-time in the field to enable the most efficient N fertilizer management system. The objective of this study was to assess if mobile in-field fluorescence sensor measurements can accurately quantify the variability of nitrogen indicators in maize canopy early in the crop growing season. A Multiplex\u00ae3 fluorescence sensor was used to collect crop canopy data at the V6 and V9 maize growth stages. Multiplex fluorescence indices were successful in discriminating variability among N treatments with moderate accuracies at V6, and higher at the V9 stage. Fluorescence-based indices were further utilized with a machine learning (ML) model to estimate canopy nitrogen indicators i.e., N concentration and above-ground biomass at the V6 and V9 growth stages independently. Parameter estimation using the Support Vector Regression (SVR)-based ML mode indicated a promising accuracy in estimation of N concentration and above-ground biomass at the V6 stage of maize with the moderate range of correlation coefficient (r = 0.72 \u00b1 0.03) and Root Mean Square Error (RMSE). The retrieval accuracies (r = 0.90 \u00b1 0.06) at the V9 stage were better than those of the V6 growth stage with a reasonable range of error estimates and yielding the lowest RMSE (0.23 (%N) and 12.37 g (biomass)) for all canopy N indicators. Mobile fluorescence sensing can be used with reasonable accuracies for determining canopy N variability at early growth stages of maize, which would help farmers in optimal management of nitrogen.<\/jats:p>","DOI":"10.3390\/rs14205077","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T02:10:27Z","timestamp":1665540627000},"page":"5077","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Assessing Nitrogen Variability at Early Stages of Maize Using Mobile Fluorescence Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Rafael","family":"Siqueira","sequence":"first","affiliation":[{"name":"Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-7125","authenticated-orcid":false,"given":"Dipankar","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-6094","authenticated-orcid":false,"given":"Louis","family":"Longchamps","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences Section, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Raj","family":"Khosla","sequence":"additional","affiliation":[{"name":"Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA"},{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"ref_1","unstructured":"Gupta, M.L., and Khosla, R. 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