{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T06:27:11Z","timestamp":1786688831820,"version":"build-2736575974"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Rapid, non-destructive, and accurate diagnosis of nitrogen (N) nutritional status in greenhouse cucumber production is critical to mitigate over-fertilization risks and support sustainable intensification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An integrated diagnostic framework was developed by coupling the  agronomic critical nitrogen (Nc) dilution curve with a hybrid Competitive Adaptive Reweighted Sampling and Variance Inflation Factor (CARS-VIF) feature selection algorithm, followed by benchmarking six machine learning regression models, including K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Extra Trees, Random Forest (RF), and Gradient Boosting Regression (GBR) .<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The critical nitrogen dilution curve was parameterized as Nc = 4.378 \u00d7 DW-0.115 (R\u00b2 = 0.745), with empirical Nitrogen Nutrition Index (NNI) values ranging from 0.72 to 1.22. The CARS-VIF pipeline compressed the full spectrum down to four sensitive wavebands (430, 677, 688, and 953 nm), achieving a 99.81% dimensionality reduction. GBR emerged as the optimal inversion model, delivering a validation R\u00b2 of 0.845, RMSE of 0.061, and the lowest MAE of 0.046.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This non-destructive framework holds promise for future integration into automated greenhouse sensor platforms or unmanned aerial vehicles (UAVs), offering a powerful digital toolkit to support precision fertilization decision-making and sustainable smart facility horticulture. Additional validation under field conditions is required before operational deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1909263","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:32:49Z","timestamp":1786685569000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Coupling critical nitrogen dilution curve with hyperspectral feature optimization and machine learning for inversion of nitrogen nutrition index in greenhouse cucumber"],"prefix":"10.3389","volume":"17","author":[{"given":"Tingting","family":"Zhao","sequence":"first","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Hu","sequence":"additional","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiming","family":"Xiu","sequence":"additional","affiliation":[{"name":"Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"101055","DOI":"10.1016\/j.pecs.2022.101055","article-title":"Reduction in greenhouse gas and other emissions from ship engines: Current trends and future options","volume":"94","author":"Aakko-Saksa","year":"2023","journal-title":"Prog. 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