Hyperspectral inversion of winter wheat nitrogen content based on feature optimization and stacking ensemble learning.

Fuxing Guan, Changchun Li, Guijun Yang, Yinghua Jiao, Guangsheng Zhang, Dan Li, Tengyue Cui

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Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Fuxing GuanSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.

Changchun LiSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.

Guijun YangCollege of Geological Engineering and Geomatics, Chang'an University, Xi'an, China.

Yinghua JiaoShandong Provincial Institute of Land Surveying and Mapping, Jinan, China.

Guangsheng ZhangSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.

Dan LiCollege of Surveying and Mapping Engineering, Heilongjiang Institute of Technology, Harbin, Heilongjiang, China.

Tengyue CuiSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

ensemble learningfeature selectionhyperspectral remote sensingmachine learningnitrogenSHAP analysiswinter wheat

Identifiers

PMID42745986

DOI10.3389/fpls.2026.1911340

PMCPMC13574828

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.