Simultaneous species and sex identification of silkworm pupae using hyperspectral imaging technology. Issue 8 (14th September 2018)
- Record Type:
- Journal Article
- Title:
- Simultaneous species and sex identification of silkworm pupae using hyperspectral imaging technology. Issue 8 (14th September 2018)
- Main Title:
- Simultaneous species and sex identification of silkworm pupae using hyperspectral imaging technology
- Authors:
- Tao, Dan
Wang, Zhengrong
Li, Guanglin
Xie, Lin - Abstract:
- Abstract: To obtain high-quality raw silk and improve the economic values of sericulture industry, sex needs to be discriminated first before cross-breeding. Much work has been reported about sex identification. However, to realize automatic separation of silkworm pupae, the species also needs to be classified, which no research has ever explored. Hence, this paper studied the feasibility of visible and near-infrared hyperspectral imaging technology to identify the species and sex of silkworm pupae. 288 hyperspectral images of silkworm pupae were collected and the average spectra were extracted from the region of interest, around the tail region of silkworm pupae. Successive projection algorithm was served as a variable selection method to choose the optimal wavelengths from the full spectra. At the same time, principal component analysis was used to choose the characteristic images. Then, the gray-level co-occurrence matrix was implemented on the first three principal component images (accounted for 99.05% of the total variances) to extract 48 textural features. Partial least squares discriminant analysis and support vector machine models were built, respectively, based on the spectral data, textural data and fusion data that included spectral and textural data, in which the support vector machine model based on the fusion data, gave the best species and sex identification result with an accuracy of 95.83%. It demonstrated that the hyperspectral imaging technology could beAbstract: To obtain high-quality raw silk and improve the economic values of sericulture industry, sex needs to be discriminated first before cross-breeding. Much work has been reported about sex identification. However, to realize automatic separation of silkworm pupae, the species also needs to be classified, which no research has ever explored. Hence, this paper studied the feasibility of visible and near-infrared hyperspectral imaging technology to identify the species and sex of silkworm pupae. 288 hyperspectral images of silkworm pupae were collected and the average spectra were extracted from the region of interest, around the tail region of silkworm pupae. Successive projection algorithm was served as a variable selection method to choose the optimal wavelengths from the full spectra. At the same time, principal component analysis was used to choose the characteristic images. Then, the gray-level co-occurrence matrix was implemented on the first three principal component images (accounted for 99.05% of the total variances) to extract 48 textural features. Partial least squares discriminant analysis and support vector machine models were built, respectively, based on the spectral data, textural data and fusion data that included spectral and textural data, in which the support vector machine model based on the fusion data, gave the best species and sex identification result with an accuracy of 95.83%. It demonstrated that the hyperspectral imaging technology could be a new and nondestructive method to replace the manual work. … (more)
- Is Part Of:
- Spectroscopy letters. Volume 51:Issue 8(2018)
- Journal:
- Spectroscopy letters
- Issue:
- Volume 51:Issue 8(2018)
- Issue Display:
- Volume 51, Issue 8 (2018)
- Year:
- 2018
- Volume:
- 51
- Issue:
- 8
- Issue Sort Value:
- 2018-0051-0008-0000
- Page Start:
- 446
- Page End:
- 452
- Publication Date:
- 2018-09-14
- Subjects:
- Hyperspectral imaging -- identification -- sex -- silkworm pupa -- species
Spectrum analysis -- Periodicals
543.5 - Journal URLs:
- http://www.tandfonline.com/ ↗
- DOI:
- 10.1080/00387010.2018.1503602 ↗
- Languages:
- English
- ISSNs:
- 0038-7010
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8411.120000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 9427.xml