Home / Current Issue / Paper 1719575
Automated Non-Contact Measurement System for Fish Surface Area and Volume in Industrial Processing Lines
Subject area: Science,Engineering and Technology · Area of research: Industrial Automation & Smart Manufacturing
DOI: https://doi.org/10.64388/IREV10I1-1719575
Abstract
This study presents the development of an automated, non-contact measurement system designed to estimate the surface area and volume of fish within dynamic industrial processing lines. Traditional manual measurement methods are often invasive, time-consuming, and incompatible with high-speed production environments. To overcome these limitations, a computer vision-based system utilizing 3D-coordinate mapping was developed. A significant technical advancement in this research is the integration of a predictive Kalman filter algorithm to stabilize coordinate extraction against mechanical vibrations and motion blur inherent in moving conveyor systems. The methodology involves real-time point cloud reconstruction of the fish geometry, followed by mathematical integration to calculate morphometric parameters. Experimental validation shows that the automated system achieves high precision, with an average error of less than 0.6% compared to the manual partition method. The results demonstrate that the proposed system provides a robust, high-throughput solution for real-time quality control and dosage calculation in the fish processing industry. This work bridges the gap between static laboratory-scale measurements and fully automated industrial applications.
Keywords
Automated Measurement, 3D-Coordinate Mapping, Industrial Conveyor, Kalman Filter, Non-Contact Sensing
References
[1] P. Ideia, J. Pinto, R. Ferreira, L. Figueiredo, V. Spínola, and P. C. Castilho, “Fish Processing Industry Residues: A Review of Valuable Products Extraction and Characterization Methods,” Waste and Biomass Valorization, vol. 11, no. 7. pp. 3223–3246, 2020. 10.1007/s12649-019-00739-1.
[2] R. M. Khisamutdinov and M. R. Khisamutdinov, “Automation system goals for the creation and operation of the tool,” in IOP Conference Series: Materials Science and Engineering, 2014. 10.1088/1757-899X/69/1/012021.
[3] T. C. Wehner and C. H. Miller, “Efficiency of Single-harvest Methods for Measurement of Yield in Fresh-market Cucumbers,” J. Am. Soc. Hortic. Sci., vol. 109, no. 5, pp. 659–664, 2022,
[4] W. Liu et al., “Cutting Techniques in the Fish Industry: A Critical Review,” Foods, vol. 11, no. 20. 2022.
[5] A. Getu and K. Misganaw, “Post-harvesting and Major Related Problems of Fish Production,” Fish. Aquac. J., vol. 06, no. 04, 2015, 10.4172/2150-3508.1000154.
[6] B. Farahmand, E. E. Takamjani, H. R. Yazdi, H. Saeedi, M. Kamali, and M. B. Cham, “A systematic review on the validity and reliability of tape measurement method in leg length discrepancy,” Med. J. Islam. Repub. Iran, vol. 33, no. 1, 2019,
[7] M. Saqib, R. Bernhardt, M. Kästner, N. Beshchasna, G. Cuniberti, and J. Opitz, “Determination of the entire stent surface area by a new analytical method,” Materials (Basel)., vol. 13, no. 24, pp. 1–11, 2020, 10.3390/ma13245633.
[8] K. Akila, B. Sabitha, K. Balamurugan, K. Balaji, and T. Ashwin Gourav, “Mechatronics system design for automated chilli segregation,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 8, pp. 546–550, 2019.
[9] J. Rantung, M. T. Tran, H. Y. Jang, J. W. Lee, H. K. Kim, and S. B. Kim, “Determination of the Fish Surface Area and Volume Using Ellipsoid Approximation Method Applied for Image Processing,” Lect. Notes Electr. Eng., vol. 465, pp. 334–347, 2017, 69814-4_33.
[10] J. Rantung, J. M. Oh, H. K. Kim, S. J. Oh, and S. B. Kim, “Real-Time Image Segmentation and Determination of 3D Coordinates for Fish Surface Area and Volume Measurement based on Stereo Vision,” J. Inst. Control. Robot. Syst., vol. 24, no. 2, pp. 141–148, 2018, 10.5302/J.ICROS.2018.17.0213.
[11] A. Daroux, F. Martignac, M. Nevoux, J. L. Baglinière, D. Ombredane, and J. Guillard, “Manual fish length measurement accuracy for adult river fish using an acoustic camera (DIDSON),” J. Fish Biol., vol. 95, no. 2, pp. 480–489, 2019,
[12] M. Petrtýl, L. Kalous, and D. Memiş, “Comparison of manual measurements and computer-assisted image analysis in fish morphometry,” Turkish J. Vet. Anim. Sci., vol. 38, no. 1, pp. 88–94, 2013, 1209-9.
[13] M. T. Tran, H. H. Nguyen, J. Rantung, H. K. Kim, S. J. Oh, and S. B. Kim, “A New Approach of 2D Measurement of Injury Rate on Fish by a Modified K-means Clustering Algorithm Based on L*A*B* Color Space,” Lect. Notes Electr. Eng., vol. 465, pp. 324–333, 2017, 10.1007/978-3-319-69814-4_32.
[14] B. Y. Lee, Y. Y. Kim, S. T. Yi, and J. K. Kim, “Automated image processing technique for detecting and analysing concrete surface cracks,” Struct. Infrastruct. Eng., vol. 9, no. 6, pp. 567– 577, 2013,
[15] F. Li, X. Li, H. Huang, H. Xiang, C. Guan, and M. Guan, “An Image Processing Method for Measuring the Surface Area of Rapeseed Pods,” Appl. Sci., vol. 13, no. 8, 2023, 10.3390/app13085129.
[16] S. Derafshpour, M. Valizadeh, A. Mardani, and M. Tamaddoni Saray, “A novel system developed based on image processing techniques for dynamical measurement of tire-surface contact area,” Meas. J. Int. Meas. Confed., vol. 139, pp. 270 –276, 2019, 10.1016/j.measurement.2019.02.074.
[17] V. G. Narushin, G. Lu, J. Cugley, M. N. Romanov, and D. K. Griffin, “A 2-D imaging- assisted geometrical transformation method for non-destructive evaluation of the volume and surface area of avian eggs,” Food Control, vol. 112, 2020, 10.1016/j.foodcont.2020.107112.
[18] 2015 R Castaneda, BE Battah - US Patent App. 14/798, 402, Systems and methods for electronically obtaining a fish length. [Online]. Available: https://patentimages.storage.googleapis.com/e6/ 3e/17/68691b5f2275a3/US20150316367A1.pdf
[19] P. Muñoz-Benavent, G. Andreu-García, J. M. Valiente-González, V. Atienza-Vanacloig, V. Puig-Pons, and V. Espinosa, “Automatic Bluefin Tuna sizing using a stereoscopic vision system,” ICES J. Mar. Sci., vol. 75, no. 1, 2018, 10.1093/icesjms/fsx151.
[20] M. R. Shortis et al., “A review of techniques for the identification and measurement of fish in underwater stereo-video image sequences,” in Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 2013. 10.1117/12.2020941.
[21] V. Atienza-Vanacloig, G. Andreu-García, F. López-García, J. M. Valiente-González, and V. Puig-Pons, “Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model,” Comput. Electron. Agric., vol. 130, 2016,
[22] F. Shafait et al., “Towards automating underwater measurement of fish length: A comparison of semi-automatic and manual stereo-video measurements,” ICES J. Mar. Sci., vol. 74, no. 6, 2017, 10.1093/icesjms/fsx007.
[23] M. Zhang et al., “Research on evaluation method of stereo vision measurement system based on parameter-driven,” Optik (Stuttg)., vol. 245, 2021,
[24] M. Hao, H. Yu, and D. Li, “The measurement of fish size by machine vision - A review,” in IFIP Advances in Information and Communication Technology, 2016. 48354-2_2.
[25] V. Leemans, B. Dumont, and M. F. Destain, “Assessment of plant leaf area measurement by using stereo-vision,” in 2013 International Conference on 3D Imaging, IC3D 2013 - Proceedings, 2013. 10.1109/IC3D.2013.6732085.
[26] Y. Song, C. A. Glasbey, G. Polder, and G. W. A. M. van der Heijden, “Non-destructive automatic leaf area measurements by combining stereo and time-of-flight images,” IET Comput. Vis., vol. 8, no. 5, 2014,
[27] N. Bandi, R. B. Tunyogi, Z. Szabo, E. Farkas, and C. Sulyok, “Image-Based Volume Estimation Using Stereo Vision,” in SISY 2020 - IEEE 18th International Symposium on Intelligent Systems and Informatics, Proceedings, 2020, pp. 55 –60. 10.1109/SISY50555.2020.9217089.
[28] Y. Zhang et al., “Accurate profile measurement method for industrial stereo-vision systems,” Sens. Rev., vol. 40, no. 4, pp. 445–453, 2020, 10.1108/SR-04-2019-0104.
[29] H. Kieu, T. Pan, Z. Wang, M. Le, H. Nguyen, and M. Vo, “Accurate 3D shape measurement of multiple separate objects with stereo vision,” Meas. Sci. Technol., vol. 25, no. 3, 2014, 10.1088/0957-0233/25/3/035401.
[30] J. Rantung, F. P. Sappu, and Y. Tondok, “Real- time Measurement Method for Fish Surface Area and Volume Based on Stereo Vision,” Adv. Sci. Technol. Eng. Syst. J., vol. 6, no. 5, pp. 141–148, 2021,
How to cite this paper
@article{1719575,
author = {Jotje Rantung, Gideon David Rantung},
title = {Automated Non-Contact Measurement System for Fish Surface Area and Volume in Industrial Processing Lines},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {698-706},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719575.pdf},
abstract = {This study presents the development of an automated, non-contact measurement system designed to estimate the surface area and volume of fish within dynamic industrial processing lines. Traditional manual measurement methods are often invasive, time-consuming, and incompatible with high-speed production environments. To overcome these limitations, a computer vision-based system utilizing 3D-coordinate mapping was developed. A significant technical advancement in this research is the integration of a predictive Kalman filter algorithm to stabilize coordinate extraction against mechanical vibrations and motion blur inherent in moving conveyor systems. The methodology involves real-time point cloud reconstruction of the fish geometry, followed by mathematical integration to calculate morphometric parameters. Experimental validation shows that the automated system achieves high precision, with an average error of less than 0.6% compared to the manual partition method. The results demonstrate that the proposed system provides a robust, high-throughput solution for real-time quality control and dosage calculation in the fish processing industry. This work bridges the gap between static laboratory-scale measurements and fully automated industrial applications.},
keywords = {Automated Measurement, 3D-Coordinate Mapping, Industrial Conveyor, Kalman Filter, Non-Contact Sensing},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1719575}
}