Hyperspectral data-driven corn nitrogen monitoring: application and interpretability analysis of multi-source feature optimization and stacked ensemble learning methods
Haoquan Kong, Yingnan Gu, Pu Zhao, Yanyan Zheng, Li Tian, Qinghui Dong · 2026
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Description
Haoquan Kong and co-authors built a new way to track nitrogen in corn leaves from light data in 2026. Good nitrogen checks help farms raise yields while using less feed for the soil. The team worked with a corn type grown in Heilongjiang Province, China, and gathered rich light scans from the crop canopy.
They compared ways to pick the best light bands and built new index scores from the scans. A stacked model that blends three base learners and one top learner gave the best fit for leaf nitrogen. Bands near the red edge and the near infrared range mattered most, which fits what is known about how plants use nitrogen.
Catalog record
| CREATOR | Haoquan Kong, Yingnan Gu, Pu Zhao, Yanyan Zheng, Li Tian, Qinghui Dong |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 64 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |