Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches
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| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Springer
2023-02-01
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| Subjects: | Grain quality, Machine learning, Nitrogen, Random forest, Short-wave infrared, Yield prediction, |
| Online Access: | http://hdl.handle.net/10261/310698 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012818 https://api.elsevier.com/content/abstract/scopus_id/85147176982 |
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