{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:14:42Z","timestamp":1788243282227,"version":"build-2803163510"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    Rapid, non-destructive crop nitrogen monitoring is a core pillar of precision fertilization and mass-balance nutrient management, which requires real-time uptake data to balance fertilizer inputs, crop demand and environmental nitrogen losses. But hyperspectral data redundancy, background noise and black-box machine learning models limit inversion generalization performance and interpretability, hampering field-scale mass-balance management. Using jointing-to-filling winter wheat canopy hyperspectral data, we built a three-tier progressive framework of preprocessing, feature selection and model architecture, systematically evaluating 2 preprocessing, 5 feature selection and 5 modeling schemes, with SHAP-based analysis to reveal the physiological mechanisms underlying band contributions. The main findings are summarized as follows: (1) First-derivative (FD) transformation achieved selective enhancement of nitrogen signals. By removing indirect coupling confounding signals, the test-set\n                    <jats:italic>R<\/jats:italic>\n                    \u00b2 of all models increased from 0.57\u20130.73 under Savitzky\u2013Golay (SG) full-spectrum input to 0.71\u20130.76. (2) Feature selection delivered a significantly greater accuracy gain than model optimization, and a clear feature-model compatibility pattern was identified. Specifically, the sparse high-purity features selected by LASSO were highly aligned with the similarity measurement mechanism of the GPR kernel function. (3) The FD\u2013LASSO\u2013stacking ensemble scheme achieved the optimal inversion performance, with a test-set\n                    <jats:italic>R<\/jats:italic>\n                    \u00b2 of 0.875, RMSE of 0.281%, and RPD of 2.834. This model also achieved favorable performance with an R\n                    <jats:sup>2<\/jats:sup>\n                    of 0.781 in cross-growth-stage inversion, which verifies its generalization ability and stability to a certain extent.(4) SHAP analysis quantified the contribution levels of features screened by each algorithm, and identified cross-algorithm common core bands including the 757 nm red edge and 939 nm near-infrared bands, verifying the biological rationality of model decisions. This study not only realizes high-accuracy nitrogen inversion in winter wheat, but also deepens the mechanistic understanding of hyperspectral inversion from the perspectives of feature-model compatibility and model interpretability. It provides theoretical and technical support for lightweight sensor band design, field precision nitrogen diagnosis, and mass-balance-based nitrogen management.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1911340","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:32:45Z","timestamp":1788240765000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyperspectral inversion of winter wheat nitrogen content based on feature optimization and stacking ensemble learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Fuxing","family":"Guan","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University","place":["Jiaozuo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University","place":["Jiaozuo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghua","family":"Jiao","sequence":"additional","affiliation":[{"name":"Shandong Provincial Institute of Land Surveying and Mapping","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University","place":["Jiaozuo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Surveying and Mapping Engineering, Heilongjiang Institute of Technology","place":["Harbin, Heilongjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengyue","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University","place":["Jiaozuo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2026.112093","article-title":"Integrating feature selection and machine learning for nitrogen assessment in grapevine leaves using in-field hyperspectral imaging","volume":"252","author":"Asad","year":"2026","journal-title":"Comput. 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