An Image-based Eri Silkworm Pupa Grading Method Using Shape, Color, and Size

Kiattisin Kanjanawanishkul

doi:10.5875/ausmt.v12i1.2331

An Image-based Eri Silkworm Pupa Grading Method Using Shape, Color, and Size

Kiattisin Kanjanawanishkul^1*

¹Research Unit of Process Design and Automation, Faculty of Engineering, Mahasarakham University, Kamriang, Kantharawichai, Mahasarakham, 44150, Thailand

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ABSTRACT

Eri silkworm pupae are well known as an alternative for a protein food source. At present, they are sold as canned food for long-term preservation. Therefore, good quality and size consistency are essential. To evaluate quality and size, an image-based grading method was proposed. The image of pupae was taken and then shape features (i.e., solidity, aspect ratio, and extent) and color features based on three color models (i.e., RGB, HSV, and L*a*b*) were extracted. Two neural networks with 10-fold cross validation were separately developed for shape evaluation and color evaluation. After misshapen and discolored pupae were identified by neural networks, remaining pupae were graded into five size numbers according to their length: very small, small, medium, large, very large. Experimental results showed that the average accuracies for shape evaluation and color evaluation were 99.64% and 99.58%, respectively. The accuracy for size evaluation was 94%. Therefore, the proposed grading method reduces sorting time and increases sorting accuracy.

Keywords: Canned Pupae; Color features; Eri Silkworm Pupae; Image Processing; Neural Networks; Pupa Grading; Shape Features

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REFERENCES

T. Longvah, K. Mangthya, and P. Ramulu, “Nutrient composition and protein quality evaluation of Eri silkworm (Samia ricinii) prepupae and pupae,” Food Chemistry, vol. 128, no. 2, pp. 400–403, 2011. https://doi.org/10.1016/j.foodchem.2011.03.041
D. Kumar, P. Dev, and R.V. Kumar, “Biomedical applications of silkworm pupae proteins,” in Biomedical Applications of Natural Proteins, D. Kumar, R. Kundapur, Ed. New Delhi: Springer, 2015, pp. 41-49.
S. Chukiatsiri, and W. Hangtrakul, “Biological activities of protein extracts from silkworm pupae against noncommunicable diseases,” in proceedings of 6th International Conference on Biochemistry and Molecular Biology (BMB2018), Rayong, Thailand, June 20-22, 2018, pp. 1-11.
P. Vaithanomsat, and C. Punyasawon, “Process optimization for the production of Philosamia ricini (Eri silk) pupae hydrolysate,” Kasetsart Journal - Natural Science, vol. 42, no. 2, pp. 341–352, 2008.
C. Srikaew, and S. Songsermpong. “Eri silkworm pupae processing,” in proceedings of 50th Kasetsart Annual Conference, Bangkok, Thailand, Jan. 31 – Feb. 4, 2012, pp. 234–243.
K. Kanjanawanishkul, P. Srinuan-on, P. Thammakit and J. Chinnakotr, “A rotary drum screen with internal screw flights for Eri silkworm pupae,” in proceedings of 2nd International Conference on Mechatronics and Mechanical Engineering, Singapore, Sept. 15-16, 2015, pp. 02003-p.1-4. https://doi.org/10.1051/matecconf/20153402003
S. Dubey, and A. Jalal, “Application of image processing in fruit and vegetable analysis: A review,” Journal of Intelligent Systems, vol. 24, no. 4, pp. 405-424, 2015. https://doi.org/10.1515/jisys-2014-0079
M. Saberioon, A. Gholizadeh, P. Cisar, A. Pautsina, and J. Urban, “Application of machine vision systems in aquaculture with emphasis on fish: state‐of‐the‐art and key issues”, Review in Aquaculture, vol. 9, no. 4, pp. 369-387, 2017. https://doi.org/10.1111/raq.12143
J. Ma, D. Sun, J. Qu, D. Liu, H. Pu, W. Gao, and X. Zeng, “Applications of computer vision for assessing quality of agri-food products: A review of recent research advances,” Critical Reviews in Food Science and Nutrition, vol. 56, no. 1, pp. 113-127, 2016. https://doi.org/10.1080/10408398.2013.873885
J.R. Cai, L.M. Yuan, B. Liu, and L. Sun, “Nondestructive gender identification of silkworm cocoons using X-ray imaging with multivariate data analysis,” Analytical Methods, vol. 6, no. 18, pp. 7224–7233, 2014. https://doi.org/10.1039/C4AY00940A
S. Sumriddetchkajorn, C. Kamtongdee, and S. Chanhorn, “Fault-tolerant optical-penetration-based silkworm gender identification,” Computers and Electronics in Agriculture, vol. 119, pp. 201–208, 2015. https://doi.org/10.1016/j.compag.2015.10.004
Z. Zhu, H. Yuan, C. Song, X. Li, D. Fang, Z. Guo, X. Zhu, W. Liu, and G. Yan, “High-speed sex identification and sorting of living silkworm pupae using near-infrared spectroscopy combined with chemometrics,” Sensors and Actuators B: Chemical, vol. 268, pp. 299-309, 2018. https://doi.org/10.1016/j.snb.2018.04.093
V. Mahesh, A. Raj, and T. Celik, “Silkworm cocoon classification using fusion of Zernike moments-based shape descriptors and physical parameters for quality egg production,” International Journal of Intelligent Systems Technologies and Applications, vol. 16, no. 3, pp. 246–268, 2017. https://doi.org/10.1504/IJISTA.2017.10006002
A. Raj, R. Sundaram, V. Mahesh, Z. Zhuang, and A. Simeone, “A multi-sensor system for silkworm cocoon gender classification via image processing and support vector machine,” Sensors, vol. 19, no. 12, 2019. https://doi.org/10.3390/s19122656
G. Bej, A. Akuli, A. Pal, T. Dey, and N. Bhattacharyya, “Quality inspection of cocoons using X-ray imaging technique,” in proceedings of International Conference on Control, Instrumentation, Energy and Communication, Calcutta, India, Jan. 31 – Feb. 2, 2014, pp. 106-110. https://doi.org/10.1109/CIEC.2014.6959059
P. Prasobhkumar, C. Francis, and S. Gorthi, “Automated quality assessment of cocoons using a smart camera based system,” Engineering in Agriculture, Environment and Food, vol. 11, no. 4, pp. 202-210, 2018. https://doi.org/10.1016/j.eaef.2018.05.002
M. Liu, R. Xu, X. Yan, F. Li, Y. Yan, Z. Song, and H. Song, “Cocoon image segmentation and stained cocoon detection based on watershed algorithm and color line chart,” International Agricultural Engineering Journal, vol. 27, no. 3, pp. 356-363, 2018.
G. D’Angelo, and S. Rampone, “Shape-based defect classification for non destructive testing,” in proceedings of 2015 IEEE Metrology for Aerospace (MetroAeroSpace), Benevento, Italy, June 4-5, 2015, pp. 406–410. https://doi.org/10.1109/MetroAeroSpace.2015.7180691
R. Gonzalez, and R. Woods, Digital image processing, 4th ed. New York: Pearson Education, 2018.
M. Thilagavathi, and S. Abirami, “Study of neural network training algorithms in detection of wood surface defects,” International Journal of Automation and Smart Technology, vol. 9, no.3, pp. 107 – 113, 2019. https://doi.org/10.5875/ausmt.v9i3.1924

ARTICLE INFORMATION

Received: 2020-07-21

Accepted: 2021-03-26
Available Online: 2022-01-01

Cite this article:

Kiattisin Kanjanawanishkul. (2022) An Image-based Eri Silkworm Pupa Grading Method Using Shape, Color, and Size. Int. j. autom. smart technol. https://doi.org/10.5875/ausmt.v12i1.2331

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