Abstract: Method for most appropriate tealeaves harvest timing with the reference to the fiber content in tealeaves which can be estimated with ground based Near Infrared (NIR) camera images is proposed. In the proposed method, NIR camera images of tealeaves are used for estimation of nitrogen content and fiber content in tealeaves. The nitrogen content is highly correlated to Theanine (amid acid) content in tealeaves. Theanine rich tealeaves taste good. Meanwhile, the age of tealeaves depend on fiber content. When tealeaves are getting old, then fiber content is increased. Tealeaf shape volume also is increased with increasing of fiber content. Fiber rich tealeaves taste not so good, in general. There is negative correlation between fiber content and NIR reflectance of tealeaves. Therefore, tealeaves quality of nitrogen and fiber contents can be estimated with NIR camera images. Also, the shape volume of tealeaves is highly correlated to NIR reflectance of tealeaf surface. Therefore, not only tealeaf quality but also harvest amount can be estimated with NIR camera images. Experimental results show the proposed method works well for estimation of appropriate tealeaves harvest timing with fiber content in the tealeaves in concern estimated with NIR camera images.

Kohei Arai, Yoshihiko Sasaki, Shihomi Kasuya and Hideto Matusura, “Appropriate Tealeaf Harvest Timing Determination Referring Fiber Content in Tealeaf Derived from Ground based Nir Camera Images” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(8), 2015. http://dx.doi.org/10.14569/IJARAI.2015.040804

@article{Arai2015,
title = {Appropriate Tealeaf Harvest Timing Determination Referring Fiber Content in Tealeaf Derived from Ground based Nir Camera Images},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2015.040804},
url = {http://dx.doi.org/10.14569/IJARAI.2015.040804},
year = {2015},
publisher = {The Science and Information Organization},
volume = {4},
number = {8},
author = {Kohei Arai and Yoshihiko Sasaki and Shihomi Kasuya and Hideto Matusura}
}