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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.
Abstract: Method for most appropriate plucking date determination based on the elapsed days after sprouting with Near Infrared: NIR reflection from Sentinel-2 data is proposed. Depending on the elapsed days after sprouting, tealeaf quality is decreasing. On the other hand, tealeaf yield is increasing with increasing of the days after sprouting. Therefore, there is most appropriate plucking date is very important. Usually, it is determined by the normalized Difference Vegetation Index: NDVI derived from handheld NDVI cameras, drone mounted NDVI cameras, and visible to NIR radiometer onboard satellites because NIR reflection and NDVI depend on tealeaf quality and yield. It, however, does not work well in terms of poor regression performance and species dependency. Moreover, it takes time consumable works for finding appropriate tealeaves from the acquired camera images. The proposed method uses only the days after sprouting. Next thing it has to do is to determination of sprouting date. In order to determine the date, optical sensor onboard Sentinel-2 data is used. Through experiment with the truth data taken at the intensive study area of the Oita Prefectural Agriculture, Forestry and Fisheries Research Guidance Center: OPAFFRGC, it is found that the proposed method is validated.
Kohei Arai and Yoshiko Hokazono, “Method for Most Appropriate Plucking Date Determination based on the Elapsed Days after Sprouting with NIR Reflection from Sentinel-2 Data” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120404
@article{Arai2021,
title = {Method for Most Appropriate Plucking Date Determination based on the Elapsed Days after Sprouting with NIR Reflection from Sentinel-2 Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120404},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120404},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Kohei Arai and Yoshiko Hokazono}
}
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.