Application Technique of Hyperspectral Imaging for Estimation of Grass Chemical Compositions

DOI JASI Web Site 1 Citations 25 References
  • Suzuki Yumiko
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:(Present office)Graduate School of Agriculture, Hokkaido University
  • Tanaka Katsuyuki
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:School of Veterinary Medicine, Kitasato University
  • Kato Wataru
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:(Present office)Miyagi Igu High school
  • Okamoto Hiroshi
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:Research Faculty of Agriculture, Hokkaido University
  • Kataoka Takashi
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:Research Faculty of Agriculture, Hokkaido University
  • Shimada Hiroshi
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:Akita Prefectural University
  • Sugiura Toshihiro
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:School of Veterinary Medicine, Kitasato University
  • Shima Eikichi
    Graduate School of Veterinary Medicine & Animal Sciences, Kitasato University:School of Veterinary Medicine, Kitasato University

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Other Title
  • 牧草成分推定に向けたハイパースペクトルイメージングセンサ利用技術
  • ボクソウ セイブン スイテイ ニ ムケタ ハイパースペクトル イメージング センサ リヨウ ギジュツ

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Abstract

The chemical compositions (CP, ADF and NDF) of mowed grass were estimated by hyperspectral imaging. ImSpector V10 (Spectral Imaging Ltd.) was used as a hyperspectral imaging sensor, which is an integrated combination of an imaging spectrograph and a matrix camera. The spectral range of this sensor is the wavelengths between 360 and 1010nm, and the spectral resolution is 10nm. The sensor was mounted on the roof of a vehicle (AEBI, TT33), which has a drum mower attached in the front. The hyperspectral images of a meadow field were acquired by moving the vehicle. The estimation models of grass chemical compositions were obtained by spectral data sampled from hyperspectral images and by actual grass chemical compositions. The multiple regression analysis with stepwise selection of variables was used for obtaining estimation models. The cross validation (leave-one-out method) was applied to the validation of the estimation models, and the EI (evaluation index) method was applied to the confirmation of the practical accuracy of the estimation models. In conclusion, the cross validation and the EI method showed efficacy of all estimation models.

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