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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Method for Vigor Diagnosis of Tea Trees based on Nitrogen Content in Tealeaves Relating to NDVI

Author 1: Kohei Arai
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 5, No. 10 · Published 2016

DOI: 10.14569/IJARAI.2016.051004

Abstract

Method for vigor diagnosis of tea trees based on nitrogen content in tealeaves relating to NDVI is proposed. In the proposed method, NIR camera images of tealeaves are used for estimation of nitrogen content in tealeaves. The nitrogen content is highly correlated to Theanine (amid acid) content in tealeaves. Theanine rich tealeaves taste good. Therefore, tealeaves quality can be estimated with NIR camera images. Also, leaf area of tealeaves is highly correlated to NIR reflectance of tealeaf surface. Therefore, not only tealeaf quality but also harvest mount can be estimated with NIR camera images. Experimental results shows the proposed method does work for estimation of appropriate tealeaves harvest timing with NIR camera images.

Keywords

How to Cite this Article

Arai, K. (2016). Method for Vigor Diagnosis of Tea Trees based on Nitrogen Content in Tealeaves Relating to NDVI. International Journal of Advanced Research in Artificial Intelligence, 5(10). https://doi.org/10.14569/IJARAI.2016.051004

Arai, Kohei. "Method for Vigor Diagnosis of Tea Trees based on Nitrogen Content in Tealeaves Relating to NDVI." International Journal of Advanced Research in Artificial Intelligence, vol. 5, no. 10, 2016, https://doi.org/10.14569/IJARAI.2016.051004.

@article{Arai2016,
  title     = {Method for Vigor Diagnosis of Tea Trees based on Nitrogen Content in Tealeaves Relating to NDVI},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {5},
  number    = {10},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJARAI.2016.051004},
  url       = {https://doi.org/10.14569/IJARAI.2016.051004}
}

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