Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles
The proposed approach reduces the cost and complexity of traditional HSI systems while maintaining high prediction accuracy. It facilitates efficient in situ nutrient detection and offers a promising tool for sustainable forestry development.
Authors
- Cheng Zeng (ORCID: https://orcid.org/0000-0002-6689-185X)
- Yi Wu (ORCID: https://orcid.org/0000-0001-5704-2111)
- Junxian Chen (ORCID: https://orcid.org/0000-0002-5200-2871)
- Teng Long (ORCID: https://orcid.org/0000-0003-3519-7790)
- Jun Du (ORCID: https://orcid.org/0000-0002-2387-0389)
- Xiaoliang Che (ORCID: https://orcid.org/0000-0002-9967-6742)
- Yuanhang Li (ORCID: https://orcid.org/0000-0001-5819-3456)
- Tianyi Liu (ORCID: https://orcid.org/0000-0002-2484-2478)
- Yongbing Long (ORCID: https://orcid.org/0000-0001-7956-8173)
- Jing Zhao (ORCID: https://orcid.org/0000-0002-8528-1045)
- Yubin Lan
Institutions
- South China Agricultural University (CN)
- National University "Kyiv Aviation Institute" (UA)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-08-11
- DOI
- https://doi.org/10.3389/fpls.2025.1630758
- Citations
- 1
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.75