Object Detection for Katsuwonus Pelamis based on ExeML ModelArts

Authors

  • Suryadiputra Liawatimena Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia 10480
  • Franz Adeta Junior Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia 10480
  • Derrell Rizqullah Hasan Computer Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia 11480

Keywords:

Data collection, Deep learning, Object detection, Sea fisheries, Sustainable fishing

Abstract

Indonesia is one of the countries with the largest export of fish commodities in the world. Katsuwonus Pelamis (Cakalang or Tuna Skipjack) and Euthynnus Affinis (Tongkol or Mackarel tuna) have high economic value and are Indonesia's most significant foreign exchange earners. There is a need to measure the length and weight of the caught Katsuwonus Pelamis to help the Ministry of Marine Affairs and Fisheries achieve sustainable fishing. The existing method still relies on traditional data recorded by the person conducting the survey, and the calculations are frequently inaccurate. This paper proposed an object detection for Katsuwonus Pelamis based on ExeML ModelArts. The 4.000 images were divided into 80:20 for the training and validation set. The overall average errors are 5.19% for the estimated length of the fish and 21.98% for the estimated weight.

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References

H. Muluk, J. Hudiyana, and M. A. Shadiqi, “The development of psychology of culture in Indonesia,” in Asia-Pacific Perspectives on Intercultural Psychology, 2018, pp. 140–156.

D. S. Pratomo, “The Analysis of Domestic Travelers in Indonesia,” JEJAK, vol. 10, no. 2, pp. 317–329, 2017, doi: 10.15294/jejak.v10i2.11296.

A. M. A. Khan, T. S. Gray, A. C. Mill, and N. V. C. Polunin, “Impact of a fishing moratorium on a tuna pole-and-line fishery in eastern Indonesia,” Mar. Policy, vol. 94, pp. 143–149, 2018, DOI: 10.1016/j.marpol.2018.05.014.

R. Lasabuda, “PEMBANGUNAN WILAYAH PESISIR DAN LAUTAN DALAM PERSPEKTIF NEGARA KEPULAUAN REPUBLIK INDONESIA,” J. Ilm. PLATAX, vol. 1, no. 2, p. 92, 2013, doi: 10.35800/jip.1.2.2013.1251.

I. Jatmiko, R. R. Zedta, M. Agustina, and B. Setyadji, “Genetic Diversity and Demography of Skipjack Tuna (Katsuwonus pelamis) In Southern and Western Part of Indonesian Waters,” ILMU Kelaut. Indones. J. Mar. Sci., vol. 24, no. 2, p. 61, 2019, doi: 10.14710/ik.ijms.24.2.61-68.

N. Tran et al., “Indonesian aquaculture futures: An analysis of fish supply and demand in Indonesia to 2030 and role of aquaculture using the AsiaFish model,” Mar. Policy, vol. 79, pp. 25–32, 2017, DOI: 10.1016/j.marpol.2017.02.002.

S. Liawatimena et al., “A Fish Classification on Images using Transfer Learning and Matlab,” in 1st 2018 Indonesian Association for Pattern Recognition International Conference, INAPR 2018 - Proceedings, 2019, pp. 108–112, DOI: 10.1109/INAPR.2018.8627007.

S. Yuniarta, P. A. M. van Zwieten, R. A. Groeneveld, S. H. Wisudo, and E. C. van Ierland, “Uncertainty in catch and effort data of small- and medium-scale tuna fisheries in Indonesia: Sources, operational causes, and magnitude,” Fish. Res., vol. 193, pp. 173–183, 2017, DOI: 10.1016/j.fishres.2017.04.009.

A. Karman, S. Martasuganda, M. F. A. Sondita, and M. S. Baskoro, “BIOLOGICAL BASE OF SKIPJACK AS THE FOUNDATION FOR SUSTAINABLE FISHERIES MANAGEMENT IN THE NORTH MALUKU PROVINCE,” J. Ilmu dan Teknol. Kelaut. Trop., vol. 8, no. 1, pp. 159–173, 2016, doi: 10.29244/jitkt.v8i1.12713.

F. Rochman, B. Nugraha, and A. Wujdi, “PENDUGAAN PARAMETER POPULASI IKAN CAKALANG ( Katsuwonus pelamis , Linnaeus , 1758 ) DI SAMUDERA HINDIA SELATAN JAWA THE ESTIMATION ON POPULATION PARAMETERS OF SKIPJACK ( Katsuwonus pelamis , Linnaeus , 1758 ) IN THE INDIAN OCEAN SOUTH OF JAVA,” BAWAL Widya Ris. Perikan. Tangkap, vol. 7, no. 140, pp. 77–85, 2015.

J. S. Kekenusa and M. S. Paendong, “ANALISIS PENENTUAN MUSIM PENANGKAPAN IKAN CAKALANG (Katsuwonus pelamis L.) DI PERAIRAN BELANG MINAHASA TENGGARA-SULAWESI UTARA,” J. Ilm. SAINS, vol. 16, no. 2, 2016, doi: 10.35799/jis.16.2.2016.14176.

D. A. Syahrani, M. A. Musadieq, and A. Darmwan, “Analisis Peran Kebijakan Illegal, Unreported and Unregulated Fishing (IUU) pada Ekspor Ikan Tuna dan Udang Tangkap,” J. Adm. Bisnis, vol. 45, no. 1, 2017.

I. Chapsos and S. Hamilton, “Illegal fishing and fisheries crime as a transnational organized crime in Indonesia,” Trends Organ. Crime, vol. 22, no. 3, 2019, doi: 10.1007/s12117-018-9329-8.

T.Imam, U. Bala, M. L. Balarabe, and T. I. Oyeyi, “Length-weight relationship and condition factor of four fish species from Wasai Reservoir in Kano, Nigeria,” Niger. J. Anim. Prod., vol. 44, no. 3, pp. 135–139, 2021, doi: 10.51791/njap.v44i3.754.

H. Muhammad, Z. Iqbal, Q. Bashir, and M. A. Hanif, “Length-weight relationship and condition factor of catfish species from Indus River, Pakistan,” Punjab Univ. J. Zool., vol. 32, no. 1, 2017.

M. S. B. Oliveira, L. M. A. Silva, L. Prestes, and M. Tavares-Dias, “Length-weight relationship and condition factor for twelve fish species from the igarapé Fortaleza basin, a small tributary of the Amazonas river estuary,” Acta Amaz., vol. 50, no. 1, 2020, DOI: 10.1590/1809-4392201900702.

C. Svellingen, B. Totland, D. White, and J. Øvredal, “Automatic species recognition, length measurement, and weight determination, using the CatchMeter computer vision system,” 10 S., 2006.

R. Kumar et al., “Length-based population characteristics and fishery of skipjack tuna, Katsuwonus pelamis (Linnaeus, 1758) from Tuticorin waters, Tamil Nadu, India,” Indian J. Geo-Marine Sci., vol. 48, no. 1, 2019.

A. Damora, F. Fazilla, A. W. Perdana, A. Rahmah, R. M. Aprilla, and S. Salmarika, “Population dynamics of skipjack tuna (Katsuwonus pelamis) in the northern and western waters of Aceh,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 674, no. 1, DOI: 10.1088/1755-1315/674/1/012089.

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, vol. 479, DOI: 10.1007/978-3-319-48354-2_2.

X. Li, M. Shang, H. Qin, and L. Chen, “Fast, accurate fish detection and recognition of underwater images with Fast R-CNN,” 2016, DOI: 10.23919/oceans.2015.7404464.

Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object Detection in 20 Years,” CVPR, 2019.

L. Liu et al., “Deep Learning for Generic Object Detection: A Survey,” Int. J. Comput. Vis., vol. 128, no. 2, 2020, doi: 10.1007/s11263-019-01247-4.

S. Jin, X. Yan, H. Zhang, and W. Fan, “Weight-length relationships and Fulton’s condition factors of skipjack tuna (Katsuwonus pelamis) in the western and central Pacific Ocean,” PeerJ, vol. 2015, no. 2, pp. 1–11, 2015, DOI: 10.7717/peerj.758.

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Published

16.12.2022

How to Cite

Suryadiputra Liawatimena, Franz Adeta Junior, & Derrell Rizqullah Hasan. (2022). Object Detection for Katsuwonus Pelamis based on ExeML ModelArts. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 540–544. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2321

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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