Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms-Reference-Cited by-同舟云学术

Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms

Published:2020-12-07 Issue:4 Volume:42 Page:1311-1329
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sonobe Rei¹^ORCID,Yamashita Hiroto¹²,Mihara Harumi³,Morita Akio¹,Ikka Takashi¹

Affiliation:

1. Faculty of Agriculture, Shizuoka University, Shizuoka, Japan

2. United Graduate School of Agricultural Science, Gifu University, Gifu, Japan

3. Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan

Funder

Kurita Water and Environment Foundation

Yamazaki Spice Promotion Foundation and JSPS KAKENHI

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1826065

Reference78 articles.

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3. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra

4. A random forest guided tour

5. Bogetoft, P., and L. Otto 2020. “Package ‘Benchmarking’”. Accessed 17 August 2020. https://cran.r-project.org/web/packages/Benchmarking/Benchmarking.pdf

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