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DOI: 10.14569/IJACSA.2020.0110713
PDF

Barley Quality Estimation Method with UAV Mounted NIR Camera Data based on Regressive Analysis

Author 1: Kohei Arai
Author 2: Eisuke Kisu
Author 3: Kazuhiro Nagafuchi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

  • Abstract and Keywords
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Abstract: Barley quality estimation method with Unmanned Aerial Vehicle: UAV based Near Infrared: NIR camera data based on regressive analysis is proposed. The proposed method allows to predict barley quality, anthocyanin, β-glucan and water contents in the harvested “Daishimochi” of barley grains before the harvest. The prediction method proposed here is based on regression analysis with the Near Infrared: NIR camera data mounted on UAV which allows to estimate barley quality, anthocyanin, β-glucan and water contents in the harvested “Daishimochi” of barley grains before the harvest.. This is the first original attempt for the prediction in the world. Through experiment, it is found that water content (%), Anthocyanin content (mg Cy3G/100 g), Anthocyanin content (mg Cy3G/100 g: which corresponds to dry matter), and barley β-glucan (%) can be predicted before the harvest with high R2 value (more than 0.99). Therefore, farmers can control fertilizer and water supply for improvement of the Daishimochi barley grain quality.

Keywords: Unmanned Aerial Vehicle: UAV; Near Infrared: NIR camera; Daishimochi; anthocyanin; ß-glucan and water contents; barley quality

Kohei Arai, Eisuke Kisu and Kazuhiro Nagafuchi, “Barley Quality Estimation Method with UAV Mounted NIR Camera Data based on Regressive Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110713

@article{Arai2020,
title = {Barley Quality Estimation Method with UAV Mounted NIR Camera Data based on Regressive Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110713},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110713},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Kohei Arai and Eisuke Kisu and Kazuhiro Nagafuchi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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