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DOI: 10.14569/IJARAI.2016.051103
PDF

Method for NIR Reflectance Estimation with Visible Camera Data based on Regression for NDVI Estimation and its Application for Insect Damage Detection of Rice Paddy Fields

Author 1: Kohei Arai
Author 2: Kenji Gondoh
Author 3: Osamu Shigetomi
Author 4: Yuko Miura

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 5 Issue 11, 2016.

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Abstract: Method for Near Infrared: NIR reflectance estimation with visible camera data based on regression for Normalized Vegetation Index: NDVI estimation is proposed together with its application for insect damage detection of rice paddy fields. Through experiments at rice paddy fields which is situated at Saga Prefectural Agriculture Research Institute SPARI in Saga city, Kyushu, Japan, it is found that there is high correlation between NIR reflectance and Green color reflectance. Therefore, it is possible to estimate NIR reflectance with visible camera data which results in possibility of estimation of NDVI with drone mounted visible camera data. As is well known that the protein content in rice crops is highly correlated with NIR intensity, or reflectance of rice leaves, it is possible to estimate rice crop quality with drone based visible camera data.

Keywords: Rice crop; Rice leaf; Nitrogen content; Protein content; NIR reflectance; Water content; Size of rice leaves; Weight of rice crops

Kohei Arai, Kenji Gondoh, Osamu Shigetomi and Yuko Miura , “Method for NIR Reflectance Estimation with Visible Camera Data based on Regression for NDVI Estimation and its Application for Insect Damage Detection of Rice Paddy Fields” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(11), 2016. http://dx.doi.org/10.14569/IJARAI.2016.051103

@article{Arai2016,
title = {Method for NIR Reflectance Estimation with Visible Camera Data based on Regression for NDVI Estimation and its Application for Insect Damage Detection of Rice Paddy Fields},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2016.051103},
url = {http://dx.doi.org/10.14569/IJARAI.2016.051103},
year = {2016},
publisher = {The Science and Information Organization},
volume = {5},
number = {11},
author = {Kohei Arai and Kenji Gondoh and Osamu Shigetomi and Yuko Miura }
}



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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