Application of Fourth Industrial Revolution Technologies to Marine Aquaculture for Future Food: Imperatives, Challenges and Prospects

Saleem Mustafa

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Sitti Raehanah M. Shaleh

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Rossita Shapawi

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Abentin Estim

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Ching Fui Fui

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Ag. Asri Ag. Ibrahim

Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Audrey Daning Tuzan

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Lim Leong Seng

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Chen Cheng Ann

Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Alter Jimat

Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, 88400, Malaysia

Burhan Japar

Korporasi Kemajuan Perikanan dan Nelayan (KO-NELAYAN), Wisma Pertanian Sabah, Kota Kinabalu, Sabah, 88994,Malaysia

DOI: https://doi.org/10.36956/sms.v3i1.378


Abstract

This study was undertaken to examine the options and feasibility of deploying new technologies for transforming the aquaculture sector with the objective of increasing the production efficiency. Selection of technologies to obtain the expected outcome should, obviously, be consistent with the criteria of sustainable development. There is a range of technologies being suggested for driving change in aquaculture to enhance its contribution to food security. It is necessary to highlight the complexity of issues for systems approach that can shape the course of development of aquaculture so that it can live-up to the expected fish demand by 2030 in addition to the current quantity of 82.1 million tons. Some of the Fourth Industrial Revolution (IR4.0) technologies suggested to achieve this target envisage the use of real-time monitoring, integration of a constant stream of data from connected production systems and intelligent automation in controls. This requires application of mobile devices, internet of things (IoT), smart sensors, artificial intelligence (AI), big data analytics, robotics as well as augmented virtual and mixed reality. AI is receiving more attention due to many reasons. Its use in aquaculture can happen in many ways, for example, in detecting and mitigating stress on the captive fish which is considered critical for the success of aquaculture. While the technology intensification in aquaculture holds a great potential but there are constraints in deploying IR4.0 tools in aquaculture. Possible solutions and practical options, especially with respect to future food choices are highlighted in this paper.

Keywords: Food security; Aquaculture 4.0; Digitalization; Imitation seafood; Sustainable solutions


References

[1] SOFIA. The state of world fisheries and aquaculture. Food and Agriculture Organization, Rome, Italy, 2020.

[2] Kobayashi, M., Msangi, S., Batka, M. et al. Fish to 2030: The Role and Opportunity for Aquaculture. Aquaculture Economics and Management, 2015, 19 (3), 282-300.

[3] Cai, J. and Leung, P.S. (2017). Short-term projection of global fish demand and supply gaps. Fisheries and Aquaculture Technical Paper no. 607, Food and Agriculture Organization, Rome, Italy, 2017.

[4] FAO. The state of world fisheries and aquaculture. Food and Agriculture Organization, Rome, Italy, 2018.

[5] Mustafa, S. and Saad, S. Coral Triangle: Marine biodiversity and fisheries sustainability. In: Leal Filho W., Azul A.M., Brandli L., Lange Salvia A., Wall T. (eds) Life Below Water. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham, Switzerland, 2021.

[6] WB. Fish farms to produce nearly two-thirds of global food fish supply by 2030. The World Bank, Washington, DC, 2014.

[7] Mustafa, F.H., Bagul, A.H.B.P., Senoo, S. and Shapawi, R. A review of smart fish farming system. Journal of Aquaculture Engineering and Fisheries Research, 2016, 2 (4), 193 – 200.

[8] Vik, J.O. Digi Sal: Towards the digital salmon- from a reactive to a proactive research strategy in aquaculture. Norway University of Life Sciences, Oslo, Norway, 2016.

[9] Lu, H. D., Yu, X., & Liu, G. Q. Abnormal behavior detection method of fish school under low dissolved oxygen stress based on image processing and compressed sensing. Journal of Zhejiang University (Agriculture and Life Ences), 2018, 44(4), 499– 506.

[10] Chen, Y. Q., Li, S. F., Liu, H. M., Tao, P. Application of intelligent technology in animal husbandry and aquaculture industry. 14th International Conference on Computer Science & Education (ICCSE). IEEE, Toronto, ON, Canada, 2019.

[11] Helland, S. How digitalization is refining aquaculture research. The Fish Site. The Fish Site, Hatch Accelerator Holding Limited, Cork, Ireland, 2020.

[12] Mustafa,S., Estim, A., Shapawi, R., et al. Technological applications and adaptations in aquaculture for progress towards sustainable development and seafood security. IOP Publishing, Bristol, UK, 2021.

[13] Fore, M. Precision fish farming: A new framework to improve aquaculture, Part 1. Global Aquaculture Alliance. New Hampshire Avenue, Portsmouth, USA, 2019.

[14] Tetsuo, I. and Kobayashi, T. Smart aquaculture system: A remote feeding system with smartphones. Proceedings of the 2019 IEEE 23 International Symposium on Consumer Technologies, pages 93-96. DOI: v10.1109/ISCE.2019.8901026.

[15] Li, D. and Li, C. Intelligent aquaculture. World Aquaculture Society, Los Angeles, USA, 2021.

[16] Ogajanovski, G. Everything you need to know about neural netyworks and back propagation- machine learning easy and fun. Towards Data Science, Media, Canada, 2019.

[17] Alammar, J. A visual and interactive guide to the basics of neural networks. Creative Commons Attributions, 2018.

[18] Nizrak, A. Comparison of activation function for deep neural networks. Yildiz Technical University, Istanbul, Turkey.

[19] SOFIA. The state of world fisheries and aquaculture Food and Agriculture Organization, Rome, Italy, 2018.

[20] IFFO. Aquaculture. IFFO Marine Ingredients Organization, London, UK, 2021.

[21] Towers, L. Importance of transgenic fish to global aquaculture- a review. The Fish Site, Hatch Accelerator Holding Limited, Cork, Ireland, 2016.

[22] Muir, W.M. The threats and benefits of GM fish. EMBO Report, 2004, 5 (7), 654 – 659.

[23] Williams, D. Genetically modified salmon to hit US markets. CGTN America, Washington, DC, 2019.

[24] Tucker, J.W. Species profile: grouper aquaculture. Southern Regional Aquaculture Center (SRAC), Publication No. 721. Fort Pierce, Florida, USA, Division of Marine Science Harbor Branch Oceanographic Institution, 1999.

[25] Aerts, J. Stress in aquaculture: a rough guide. The Fish Site, Hatch Accelerator Holdings, Cork, Ireland, 2019.

[26] Fernandes, M.N., Rantin, F.T. Relationships between oxygen availability and metabolic cost of breathing in Nile tilapia (Oreochromis niloticus): aquacultural consequences. Aquaculture, 1994, 27:339–346.

[27] Moreira, P.S.A. and Volpato, G.L. Conditioning of stress in Nile tilapia. Journal of Fish Biology 2004, 64, 961–969.

[28] Jian-yu, X., Xiang-wen, M., Ying, L. et al. Behavioral response of tilapia (Oreochromis niloticus) to acute ammonia stress no0nitored by computer vision. Journal of Zhejiang University. Science, 2005, 812 – 816.

[29] Xu, J., Liu, Y., Cui, S. et al. Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved oxygen levels as monitored by computer vision. Aquaculture Engineering 2006, 35, 207 – 217.

[30] Barreto, R.E., Volpato, G.L., Faturi, C.B., et al. Aggressive behaviour traits predict physiological stress responses in Nile tilapia (Oreochromis niloticus). Marine and Freshwater Behavior and Physiology, 2009, 42,109–118.

[31] Barreto, R.E., Miyai, C.A., Sanches, F.H.C. et al. Blood cues induce antipredator behavior in Nile Tilapia conspecifics. PLoSOne, 2013, 8: e54642.

[32] Hassan, M., Zakariah, M.I., Wahab, W. et al. Histopathological and behavioral changes in Oreochromis sp. After exposure to different salinities. Journal of Fisheries cand Livestock Production, 2013, 1, 103. DOI: 10.4172/2332-2608.1000103.

[33] King, M. and Sardella, B. The effects of acclimation temperature, salinity, and behavior on the thermal tolerance of Mozambique tilapia (Oreochromis mossambicus). Journal of Experimental Zoology Part A Ecological and Integrative Physiology, 2017, 327(7) https://doi.org/10.1002/jez.2113

[34] Panase, P., Saenphet, S. and Saenphet, K. Biochemical and physiological responses of Nile tilapia, Oreochromis niloticus Lin subjected to cold shock of water temperature. Aquaculture Reports, 2018, 11, 17-23.