Posture-invariant hybrid scaling weight measurement algorithm for live eels
Keywords:
scale factor, weight measurement, non-contact, live eelsAbstract
To obtain higher economic benefits, large eel breeding companies classify live eels by weight. Due to their strong mobility and smooth body surface, living eels are not suitable for traditional mechanical weight measurement. In this study, a live eel sorting machine based on machine vision was developed, and a novel method was developed for obtaining live eel weight measurements through images. First, a backlit workbench was designed to capture static images of eels, and then the projection area and skeleton length of the images were obtained by image preprocessing. For the eel's body shape, which is generally cylindrical and gradually transitions to a flat tail, the tail posture changes affect the shape of the images; thus, a weight measurement model combining the projected area and the skeleton length was proposed. The optimal scale division coefficient of the weight model was found to be 0.745 by experimentation. Then, select eels of different weight ranges were used for model error verification and to obtain the correction function of the error. The weight gradient was used to confirm the corrected eel weight model. Finally, the system calculation results were compared with the actual measurement results. The root mean square error (RMSE) was 12.94 g, and the mean absolute percentage error (MAPE) was 2.12%. The results show that the proposed method provided a convenient, fast, and low-cost non-contact weight measurement method for live eels, reduced the damage rate of live eels, and can meet the technical requirements of actual production. Keywords: scale factor, weight measurement, non-contact, live eels DOI: 10.25165/j.ijabe.20231602.7132 Citation: Liu Q, Han Y X, Yan G Q, Mo J S, Yang Z S. Posture-invariant hybrid scaling weight measurement algorithm for live eels. Int J Agric & Biol Eng, 2023; 16(2): 207-215.References
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[4] Ma X D, Zhu K X, Guan H O, Feng J R, Yu S, Liu G. High-throughput phenotyping analysis of potted soybean plants using colorized depth images based on a proximal platform. Remote Sensing, 2019; 11(9): 1085. doi: 10.3390/rs11091085.
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[11] Koirala A, Walsh K B, Wang Z L, McCarthy C. Deep learning - Method overview and review of use for fruit detection and yield estimation. Computers and Electronics in Agriculture, 2019; 162: 219-234.
[12] Zhang Z Q. Research on freshwater fish species identification and weight prediction based on machine vision. Master dissertation. Wuhan: Huazhong Agricultural University, 2011; 66p. (in Chinese)
[13] Li L, Guo X Y. Research on fish classification method based on computer vision. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2015; 36(5): 120-124. (in Chinese)
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[19] Yang Y, Teng G H, Li B M, Shi Z X. Measurement of pig weight based on computer vision. Transactions of the CSAE, 2006; 22(2): 127-131. (in Chinese)
[20] Fu W S, Teng G H, Yang Y. Research on three-dimensional model of pig’s weight estimating. Transactions of the CSAE, 2006; 22(S2): 84-87. (in Chinese)
[21] Alikhanov J, Penchev S M, Georgieva T D, Modazhanov A, Shynybay Z, Daskalov P I. An indirect approach for egg weight sorting using image processing. Journal Of Food Measurement And Characterization, 2018; 12: 87-93.
[22] Wang J F, Luo X W, Hong T S, Ge Z Y. Application of computer vision technology in detecting mango weight and surface bruise. Transactions of the CSAE, 1998(4):186-189. (in Chinese)
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[25] Dutta M K, Issac A, Minhas N. Image processing based method to assess fish quality and freshness. Journal of Food Engineering, 2016; 177: 50-58.
[26] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man & Cybernetics, 2007; 9(1): 62-66.
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Published
2023-05-12
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Liu, Q., Han, Y., Yan, G., Mo, J., & Yang, Z. (2023). Posture-invariant hybrid scaling weight measurement algorithm for live eels. International Journal of Agricultural and Biological Engineering, 16(2), 207–215. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7132
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Information Technology, Sensors and Control Systems
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