Browse > Article
http://dx.doi.org/10.9708/jksci.2022.27.04.027

A technique for predicting the cutting points of fish for the target weight using AI machine vision  

Jang, Yong-hun (Dept. of Computer Engineering, Yeungnam University)
Lee, Myung-sub (Div. of Software&Contents, Yeungnam University College)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.4, 2022 , pp. 27-36 More about this Journal
Abstract
In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.
Keywords
AI machine vision; Image processing technology; Random sample consensus partitioning technique; Cutting point; Neural network model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Y. J. Choi, Y. W. Lee and B.G. Kim, "Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things," Big Data, Aug., 2021. DOI:10.1089/big.2020.0274   DOI
2 J. M. Miranda and M. Romero, "A prototype to measure rainbow trout's length using image processing," Aquacultural engineering, vol. 76, pp. 41-49, Aug., 2017. DOI:10.1016/j.aquaeng.2017.01.003   DOI
3 M. O. Balaban, G. F. unal Sengor, M. G. Soriano and E. G. Ruiz, "Using image analysis to predict the weight of Alaskan salmon of different species," Journal of food science, vol. 75, no. 3, pp. E157-E162, April, 2010. DOI:10.1111/j.1750-3841.2010.01522.x   DOI
4 Y. Wu, R. Zhuang and Z. Cui, "Automatic Sorting System of Large Yellow Croaker Based on Machine Vision," in Proc. IEEE Int. Conf. on High Performance Big Data and Intelligent Systems (HPBD&IS), pp. 233-237, May, 2019. DOI : 10.1109/HPBDIS. 2019.8735486   DOI
5 K. A. Kamaruzzaman, A. Mahfurdz, M. Hashim and M. N. Bidin, "Design and performance evaluation of semi-automatic fish cutting machine for industry," IOP Conference Series: Materials Science and Engineering, vol. 864, no. 1, IOP Publishing, Feb., 2020. DOI:10.1088/1757-899X/864/1/012112   DOI
6 C. U. Son, I. Kim, H. H. Tak and S. B. Lee, "Implementation of the Controller for intelligent Process System Using Neural Network," in Proc. Conf. Korean Institute of Intelligent Systems (KIIS), pp. 376-379, Nov., 2000.
7 D. A. Konovalov, A. Saleh, D. B. Efremova, J. A. Domingos and D. R. Jerry, "Automatic weight estimation of harvested fish from images," 2019 Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp. 1-7, Dec., 2019. DOI:10.1109/DICTA47822.2019.8945971   DOI
8 S. K. Kumaran, S. Mohapatra, D. P. Dogra, P. P. Roy and B. G. Kim, "Computer vision-guided intelligent traffic signaling for isolated intersections," Expert Systems with Applications, vol. 134, pp. 267-278, Nov., 2019. DOI:10.1016/j.eswa.2019.05.049   DOI
9 F. Antonucci and C. Costa. "Precision aquaculture: a short review on engineering innovations." Aquaculture International, vol. 28, no. 1, pp. 41-57, Aug., 2020. DOI :10.1007/s10499-019-00443-w   DOI
10 D. G. Lee, Y. S. Seo, "Testing cost reduction using nested mutation testing," in Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), Seogwipo, KOR, pp. 462-463, 2021.
11 M. Kobayashi, S. Msangi, M. Batka, S. Vannuccini, M. Dey, et al. "Fish to 2030: the role and opportunity for aquaculture," Aquaculture economics & management, vol. 19, no. 3 pp. 282-300, Aug., 2015. DOI:10.1080/13657305.2015.994240   DOI
12 M. Man, N. Abdullah, M. S. Rahim and I. M. Amin, "Fish Length Measurement: The Results from Different Types of Digital Camera," Journal of Advanced Agricultural Technologies, vol. 3, no.1, pp. 67-71, March, 2016. DOI:10.18178/joaat.3.1.67-71   DOI
13 M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, June, 1981. DOI:10.1145/358669.358692   DOI
14 G. Merino, M. Barange, J. L. Blanchard, J. Harle, R. Holmes, et al., "Can marine fisheries and aquaculture meet fish demand from a growing human population in a changing climate," Global Environmental Change, vol. 22, no. 4, pp. 795-806, March, 2012. DOI:10.1016/j.gloenvcha.2012.03.003   DOI
15 N.N Gaikwad, T. Ahmad, G. B. Yenge and A. Shinh, "Design, Development and Performance Evaluation of Fish Descaling Machine," Fishery Technology, vol. 54, no. 4, Oct., 2017.
16 H. Hong, X. Yang, Z. You, and F. Cheng, "Visual quality detection of aquatic products using machine vision," Aquacultural Engineering, vol. 63, pp. 62-71, Oct., 2014. DOI:10.1016/j.aquaeng.2014.10.003   DOI
17 G. Sanchez-Torres, A. Ceballos-Arroyo and S. Robles-Serrano, "Automatic measurement of fish weight and size by processing underwater hatchery images," Engineering Letters, vol. 26, no. 4, pp. 461-472, Nov., 2018.
18 H. Azarmdel, S. S. Mohtasebi, A. Jafari and A. R. Munoz, "Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision," Computers and Electronics in Agriculture, vol. 162, pp. 613-629, July, 2019. DOI:10.1016/j.compag.2019.05.005   DOI
19 I. Laradji, A. Saleh, P. Rodriguez, D. Nowrouzezahrai, M. R. Azghadi, et al. "Affinity lcfcn: Learning to segment fish with weak supervision," arXiv preprint arXiv:2011, 03149, Nov., 2020. DOI:10.48550/arXiv.2011.03149
20 T. M. Banhazi, M. Tscharke, W. M. Ferdous, C. Saunders, S. H. Lee, "Improved image analysis based system to reliably predict the live weight of pigs on farm: Preliminary results," Australian Journal of Multi-disciplinary Engineering, vol. 8, no. 2, pp. 107-119, Sep., 2011. DOI:10.1080/14488388.2011.11464830   DOI
21 S. H. Hyun, S. C. Lee, K. H. Kim and K. S. Seo, "Shape, Volume Prediction Modeling and Identical Weights Cutting for Frozen Fishes," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 3, pp. 294-299, June, 2012. DOI:10.5391/JKIIS.2012.22.3.294   DOI
22 X. Li, "Study on the Freshness Identification of Frozen-Thaw Fish Based on Hyperspectral Detection Technology," in Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), Seogwipo, KOR, pp. 96-99, 2021.
23 A. Pezzuolo, M. Guarino, L. Sartori, L. A. Gonzalez and F. Marinello, "On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera," Computers and electronics in agriculture, vol. 148, pp. 29-36, May, 2018. DOI:10.1016/j.compag.2018.03.003   DOI
24 R. A. Hauser-Davis, T. F. Oliveira, A. M. Silveira, T. B. Silva and R. L. Ziolli, "Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza)," Ecological Informatics, vol. 5, no. 6, pp. 474-478, Nov., 2010. DOI:10.1016/j.ecoinf.2010.08.002   DOI
25 T. T. E. Vo, H. Ko, J. H. Huh and Y. Kim, "Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision," Electronics, vol. 10, no. 22, 2882, Nov., 2021. DOI:10.3390/electronics10222882   DOI
26 S. H. Kim and J. H. Huh, "Consistency of medical data using intelligent neuron faster R-CNN algorithm for smart health care application," Healthcare, vol. 8, no. 2, Multidisciplinary Digital Publishing Institute, June, 2020. DOI :10.3390/healthcare8020185   DOI
27 Y. Lee, D. Jun, B. G. Kim and H. Lee, "Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network," Sensors, vol. 21, no. 10, 3351, May, 2021. DOI:10.3390/s21103351   DOI
28 W. H. Choi, "For the Indirect Measurement of Ocean Acidification, Finding a Plan to Collect Big Data of Crab Catches Using Domestic Fishing Boats: Focusing on the West Sea in South Korea," in Proc. Int. Conf. on Multimedia Information Technology and Applications (MITA), Seogwipo, KOR, pp. 458-461, 2021
29 R. JONGJARAUNSUK and W. TAPARHUDEE, "Weight Estimation of Asian Sea Bass (Lates calcarifer) Comparing Whole Body with and without Fins using Computer Vision Technique," Walailak Journal of Science and Technology (WJST), vol.18, no. 10, pp. 9495, May, 2021. DOI:10.48048/wjst.2021.9495   DOI
30 I. Nyalala, C. Okinda, N. Makange, T. Korohou, Q. Chao, et al., "On-line weight estimation of broiler carcass and cuts by a computer vision system," Poultry Science, vol. 100, no. 12, 101474, Dec., 2021. DOI:10.1016/j.psj.2021.101474   DOI
31 D. Li and L. Du, "Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish," Artificial Intelligence Review, pp. 1-40, Nov., 2021. DOI:10.1007/s10462-021-10102-3   DOI
32 J. Park, J. Y. Kim, J. H. Huh, H. S. Lee, S. H. Jung, et al., "A Novel on Conditional Min Pooling and Restructured Convolutional Neural Network," Electronics, vol. 10, no. 19, 2407, Oct., 2021. DOI:10.3390/electronics10192407   DOI
33 Q, Al-Jubouri, W. Al-Nuaimy, M. Al-Taee and I. Young, "Towards automated length-estimation of free-swimming fish using machine vision," in Proc, IEEE. Int, Conf. on Systems, Signals & Devices (SSD), pp. 469-474, March, 2017. DOI:10.1109/SSD.2017.8166931   DOI
34 S. Yang, Z. Zhang, J. Yi, J. Liu and J. Wang, "Design of bay scallop roll grading equipment," Food Research And Development, vol. 37, no. 12, pp. 220-224, 2016.
35 C. Pornpanomchai, B. Lurstwut, P. Leerasakultham and W. Kitiyanan, "Shape-and texture-based fish image recognition system." Agriculture and Natural Resources, vol. 47, no. 4 pp. 624-634, Aug., 2013.
36 A. Voulodimos, N. Doulamis, A. Doulamis and E. Protopapadakis, "Deep learning for computer vision: A brief review," Computational intelligence and neuroscience, vol. 2018, Feb., 2018. DOI:10.1155/2018/7068349   DOI