본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Postharvest biology and technology v.126, 2017년, pp.40 - 49   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Hyperspectral imaging with different illumination patterns for the hollowness classification of white radish

Pan, Leiqing (Corresponding authors. ); Sun, Ye (Corresponding authors. ); Xiao, Hui ( ); Gu, Xinzhe ( ); Hu, Pengcheng ( ); Wei, Yingying ( ); Tu, Kang ( );
  • 초록  

    Abstract This study presented the detection of hollowness in the worldwide important vegetable crop white radish ( Raphanus sativus L.) by using hyperspectral imaging covering the spectral range of 400–1000nm. The hyperspectral images based on the three illumination patterns of reflectance, transmittance, and semi-transmittance were acquired from white radishes. The successive projections algorithm (SPA) was used to identify the optimal wavelengths from the three patterns of spectra. Two classifiers of partial least square discrimination analysis (PLS-DA) and back propagation artificial neural network (BPANN) were established based on the full wavelengths and selected wavelengths. Discrimination models were performed for the two-class, three-class, and five-class hollowness classifications using the mean spectra from the regions of interest (ROI) in the spectra images. The classification results showed that hyperspectral semi-transmittance imaging combined with the BPANN model performed the best classification accuracy for the two-class hollowness classification based on the full and selected wavelengths reaching 98% and 97% for the calibration and the prediction sets, respectively. Lower accuracies were obtained for the three-class and five–class hollowness classifications based on the combination of classifiers and illumination modes. The results demonstrated that hyperspectral semi-transmittance imaging was potentially useful as a non-invasive method to identify the hollowness in white radishes. Highlights Hollowness in white radishes was detected by hyperspectral imaging. Three illumination patterns were used and compared. Important wavelengths were determined by the successive projections algorithm. PLS-DA and back propagation neural networks were used for classifying hollowness. Satisfactory classification accuracy was acquired using semi-transmittance mode.


  • 주제어

    White radish .   Hollowness .   Hyperspectral imaging .   SPA .   Classification.  

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기