Rei Sonobe - Search Results

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Year	Number of Results
2016	1
2018	1
2020	2
2021	1
2022	0

Page 1

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves.

Yamashita H, Sonobe R, Hirono Y, Morita A, Ikka T. Yamashita H, et al. Among authors: sonobe r. Sci Rep. 2021 Feb 18;11(1):4169. doi: 10.1038/s41598-021-83847-0. Sci Rep. 2021. PMID: 33603126 Free PMC article.

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.

Sonobe R, Hirono Y, Oi A. Sonobe R, et al. Plants (Basel). 2020 Mar 17;9(3):368. doi: 10.3390/plants9030368. Plants (Basel). 2020. PMID: 32192044 Free PMC article.

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.

Yamashita H, Sonobe R, Hirono Y, Morita A, Ikka T. Yamashita H, et al. Among authors: sonobe r. Sci Rep. 2020 Oct 15;10(1):17360. doi: 10.1038/s41598-020-73745-2. Sci Rep. 2020. PMID: 33060629 Free PMC article.

Assessing hyperspectral indices for tracing chlorophyll fluorescence parameters in deciduous forests.

Sonobe R, Wang Q. Sonobe R, et al. J Environ Manage. 2018 Dec 1;227:172-180. doi: 10.1016/j.jenvman.2018.06.085. Epub 2018 Sep 1. J Environ Manage. 2018. PMID: 30179805

Assessing the xanthophyll cycle in natural beech leaves with hyperspectral reflectance.

Sonobe R, Wang Q. Sonobe R, et al. Funct Plant Biol. 2016 May;43(5):438-447. doi: 10.1071/FP15325. Funct Plant Biol. 2016. PMID: 32480474

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