A multispectral imaging and semantic computing system for agricultural monitoring and analysis

Jinmika Wijitdechakul, Yasushi Kiyoki, Shiori Sasaki, Chawan Koopipat

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Multispectral image becomes widely used for environmental analysis to detect an object or phenomena that human eyes cannot capture. One of the main type of images acquired by remote sensing such as satellite or aircraft for earth observation. This paper presents a multispectral analysis for aerial images that captured by dual cameras (visible and infrared camera), which are mounted on an unmanned autonomous vehicle (UAV) or Drone. In our experiments, four spectral bands (three visible and one infrared band) were imaged, processed and analyzed to detect agricultural area and measure the health of vegetation. To interpret environmental phenomena and realize an environmental analysis, this study applies semantic analysis by creating a multispectral semantic image space, combined with three numerical indicators (the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI) and the soil adjusted vegetation index (SAVI)) that can be used to analyze plant health, photosynthetic activity and detect environmental object to determine an agricultural area. This paper also proposed the concept of multi-spectrum semantic-image space for agricultural monitoring by defining the correlation meaning from multi-dimensional parameters which related to agricultural analysis to realize and explain agriculture conditions. This paper presents the experimental study on a rice field, a cornfield, a salt farm and a coconut farm in Thailand.

本文言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXVIII
編集者Bernhard Thalheim, Hannu Jaakkola, Yasushi Kiyoki, Naofumi Yoshida
出版社IOS Press
ページ314-333
ページ数20
ISBN(電子版)9781614997191
DOI
出版ステータスPublished - 2017

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
292
ISSN(印刷版)0922-6389

ASJC Scopus subject areas

  • 人工知能

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