Analysis on Gender of Silkworms by MRI Technology

Liu, C.; Ren, Zha; Wang, H.Z.; Pan, Yang; Zhang, X.L.

doi:10.1109/bmei.2008.49

Cited by 15 publications

(8 citation statements)

References 2 publications

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“…Because the fluorescence analysis technology 6 can only be used for the gender identification of a limited number of special varieties, it cannot be widely applied. Although magnetic resonance imaging 7 and X-ray imaging technology 8 have been reported for this purpose, they are not conducive for application, since the equipment is usually expensive and bulky, and its detection accuracy is also low. Sumriddetchkajorn et al.…”

Section: Introductionmentioning

confidence: 99%

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

Yan

et al. 2021

Journal of Near Infrared Spectroscopy

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The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties (Hibiscus, Jingsong, 932, Xiang Hui, 7532×Xiang Hui, Haoyue B, Jingsong B, and 7532). The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98.44%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

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Section: Introductionmentioning

confidence: 99%

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

Yan

et al. 2021

Journal of Near Infrared Spectroscopy

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“…For example, in the non-image processing studies, Seo et al 1 weighed the mass of silkworm pupae to classify the sex of silkworm pupae with an unsatisfied accuracy of 50%. Among the image processing studies, Zhu et al, 2 Liu et al 3 and Cai et al 4 , respectively, adopted near infrared (NIR) spectroscopy, magnetic resonance imaging (MRI) and X-ray imaging techniques to determine the sex of silkworm pupae. Sumriddetchkajorn et al 5,6,7 employed a fault-tolerant optical-based system that integrated the image processing operations and the performance was as high as 95%.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

et al. 2020

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BACKGROUND Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis. RESULTS First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS‐PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS‐CARS‐PLSDA model is as high as 98.41%. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry

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“…An MRI imaging technique operating at 20MHz was used by C. Liu, Z.H. Ren [27] to determine the gender of the silkworm. The MRI image of the cocoon along with live pupa is acquired and later transformed by fast Fourier transform and T2 weighted images (to accurately reflect the tissue contrast into picture contrast) were obtained that aids in distinguishing the gender of the silkworms.…”

Section: Introductionmentioning

confidence: 99%

“…This detailed literature review indicates the existing technologies used for silkworm gender separation at different stages of their life cycle. Separation of silkworm at stages such as eggs, larvae, or pupae is not feasible in grainage centers [27,29]. X-ray or MRI images of the cocoon are high-cost alternatives which provide accurate classification, but the radiation can damage the pupa inside the cocoon.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

Raj

Sundaram

Mahesh³

et al. 2019

Sensors

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Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time.

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Analysis on Gender of Silkworms by MRI Technology

Cited by 15 publications

References 2 publications

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

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