Building a Collaborative Aquaculture Research Ecosystem with APIs and AI

Abstract

Recently, the mission of the aquaculture production sector in achieving sustainable development goals has become increasingly critical. Synthesizing large data sets with advanced technological tools in aquaculture is no longer a luxury but a necessity for significant progress. This article examines the pivotal role of Application Programming Interface (API) integration in advancing open science and collaborative research in aquaculture. It also explores the use of Artificial Intelligence (AI) to facilitate data analysis across disparate databases and proposes the establishment of a ChatGPT-like virtual environment to catalyze seamless global collaboration among researchers. A comprehensive overview is presented on the feasibility of a unified AI-driven database that collects, analyzes, and shares data, overcomes geographical constraints, and supports a shared information ecosystem. The article scrutinizes current implementations, identifies gaps in existing infrastructures, and outlines a robust framework for API integration that could significantly enhance innovation and operational efficiency in aquaculture research.

Keywords

• Artificial intelligence
• Aquaculture
• Application Programing Interface
• Internet of things

References

1. Abdul Kari, Z. ., Kabir, M. A. ., Abdul Razab, M. K. A. ., Munir, M. . B. ., Lim, P. T., & Wei, L. S. . (2020). A replacement of plant protein sources as an alternative of fish meal ingredient for African catfish, Clarias gariepinus: A review. Journal of Tropical Resources and Sustainable Science (JTRSS), 8(1), 47-59. https://doi.org/10.47253/jtrss.v8i1.164 google scholar
2. Adams, S., Henderson, R., Xin, Y., & Babyn, P. (2020). Artificial intelligence solutions for analysis of x-ray images. Canadian Association of Radiologists Journal, 72(1), 60-72. https://doi.org/10.1177/0846537120941671 google scholar
3. Ala-Pietila, P. and Smuha, N. (2021). A framework for global cooperation on artificial intelligence and its governance., 237-265. https://doi. org/10.1007/978-3-030-69128-8_15 google scholar
4. Arcelay, I., Goti, A., Oyarbide-Zubillaga, A., Akyazi, T., Alberdi, E., & Bringas, P. (2021). Definition of the future skills needs of job profiles in the renewable energy sector. Energies, 14(9), 2609. https://doi. org/10.3390/en14092609 google scholar
5. Ayling, J. and Chapman, A. (2021). Putting ai ethics to work: are the tools fit for purpose? Ai and Ethics, 2(3), 405-429. https://doi.org/10.1007/ s43681-021-00084-x google scholar
6. Cabello, F. (2006). Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment. Environmental Microbiology, 8(7), 1137-1144. https:// doi.org/10.1111/j.1462-2920.2006.01054.x google scholar
7. Chandrasekaran, G., Antoanela, N., Andrei, G., Monica, C., & Hemanth, J. (2022). Visual sentiment analysis using deep learning models with social media data. Applied Sciences, 12(3), 1030. google scholar
8. Chang, C., Wang, J., Wu, J., Hsieh, Y., Wu, T., Cheng, S., ... & Lin, C. (2021). Applying artificial intelligence (ai) techniques to implement a practical smart cage aquaculture management system. Journal of Medical and Biological Engineering. https://doi.org/10.1007/s40846-021-00621-3 google scholar

9. Chang, C., Ubina, N., Cheng, S., Lan, H., Chen, K., & Huang, C. (2022). A two-mode underwater smart sensor object for precision aquaculture based on aiot technology. Sensors, 22(19), 7603. https://doi. org/10.3390/s22197603 google scholar
10. Chen, F., Sun, M., Du, Y., Xu, J., Zhou, L., Qiu, T., . & Sun, J. (2022). Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system. Aquaculture Research, 53(12), 4401-4413. https://doi.org/10.1111/are.15938 google scholar
11. Chopin, T., Buschmann, A., Halling, C., Troell, M., Kautsky, N., Neori, A., . & Neefus, C. (2001). Integrating seaweeds into marine aquaculture systems: a key toward sustainability. Journal of Phycology, 37(6), 975986. https://doi.org/10.1046/j.1529-8817.2001.01137.x google scholar
12. Cranford, P., Kamermans, P., Krause, G., Mazurie, J., Buck, B., Dolmer, P., ... & Strand, 0. (2012). An ecosystem-based approach and management framework for the integrated evaluation of bivalve aquaculture impacts. Aquaculture Environment Interactions, 2(3), 193-213. https://doi.org/10.3354/aei00040 google scholar
13. Cunha, M., Raposo, A., & Fuks, H. (2008, April). Educational technology for collaborative virtual environments. In 2008 12th international conference on computer supported cooperative work in design (pp. 716-720). IEEE. google scholar
14. Deng, L., Chen, F., & Yuan, Y. (2022). Data and knowledge dual-driven architecture for autonomous networks. Itu Journal on Future and Evolving Technologies, 3(3), 602-611. https://doi.org/10.52953/wmup9519 google scholar
15. Dikici, E., Bigelow, M., Prevedello, L., White, R., & Erdal, B. (2020). Integrating ai into radiology workflow: levels of research, production, and feedback maturity. Journal of Medical Imaging, 7(01), 1. https:// doi.org/10.1117/1.jmi.7.1.016502 google scholar
16. Donca, I. C., Stan, O. P., Misaros, M., Gota, D., & Miclea, L. (2022). Method for continuous integration and deployment using a pipeline generator for agile software projects. Sensors, 22(12), 4637. google scholar
17. Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: yesterday, today, and tomorrow. Present and ulterior software engineering, 195-216. google scholar
18. Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., . & Williams, M. (2021). Artificial intelligence (ai): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 google scholar
19. Erol, S. (2022). Financial and economic impacts of the covid-19 pandemic on aquaculture in Türkiye and financial policy recommendations. Marine Policy, 146, 105313. https://doi.org/10.1016/j.marpol.2022.105313 google scholar
20. Fry, J., Ceryes, C., Voorhees, J., Barnes, N., & Barnes, M. (2019). Occupational safety and health in u.s. aquaculture: a review. Journal of Agromedicine, 24(4), 405-423. https://doi.org/10.1080/105992 4x.2019.1639574 google scholar
21. Gentry, R., Froehlich, H., Grimm, D., Kareiva, P., Parke, M., Rust, M., ... & Halpern, B. (2017). Mapping the global potential for marine aquaculture. Nature Ecology & Evolution, 1(9), 1317-1324. https:// doi.org/10.1038/s41559-017-0257-9 google scholar
22. Gephart, J., Golden, C., Asche, F., Belton, B., Brugere, C., Froehlich, H., . & Allison, E. (2020). Scenarios for global aquaculture and its role in human nutrition. Reviews in Fisheries Science & Aquaculture, 29(1), 122-138. https://doi.org/10.1080/23308249.2020.1782342 google scholar
23. Han, P., Lu, Q., & Li, F. (2019). A review on the use of microalgae for sustainable aquaculture. Applied Sciences, 9(11), 2377. https://doi. org/10.3390/app9112377 google scholar
24. Holetschek, J., Droege, G., Güntsch, A., Köster, N., Marquardt, J., & Borsch, T. (2019). Gardens4science: setting up a trusted network for german botanic gardens using open source technologies. Biodiversity Information Science and Standards, 3. https://doi. org/10.3897/biss.3.35368 google scholar
25. Hughes, A. (2021). Defining nature-based solutions within the blue economy: the example of aquaculture. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.711443 google scholar
26. Jeong, S., Kim, S., & Kim, J. (2020). City data hub: implementation of standard-based smart city data platform for interoperability. Sensors, 20(23), 7000. https://doi.org/10.3390/s20237000 google scholar
27. Jogdand, M. (2024). Unified ai: a revolutionary solution for content generation. International Journal of Advanced Research in Science Communication and Technology, 476-479. https://doi.org/10.48175/ ijarsct-15069 google scholar
28. Kaivo-oja, J. R. L., & Stenvall, J. (2022). A Critical Reassessment: The European Cloud University Platform and New Challenges of the Quartet Helix Collaboration in the European University System. European Integration Studies, (16), 9-23. google scholar
29. Kaur, G., Adhikari, N., Krishnapriya, S., Wawale, S., Malik, R., Zamani, A., . & Osei-Owusu, J. (2023). Recent advancements in deep learning frameworks for precision fish farming opportunities, challenges, and applications. Journal of Food Quality, 2023, 1-11. https://doi. org/10.1155/2023/4399512 google scholar
30. Kondoro, A., Rwegasira, D., Dhaou, I. B., Kelati, A., Naiman, D., Tenhunen, H., ... & Taajamaa, V. (2017). Training the future ICT innovators on open science platform. In EDULEARN17 Proceedings (pp. 19881994). IATED. google scholar
31. Kraus, K., Kraus, N., & Shtepa, O. (2021). Synergetic effects of network interconnections in the conditions of virtual reality. Journal of Entrepreneurship Management and Innovation, 17(3), 149-188. https://doi.org/10.7341/20211735 google scholar
32. Kukyte, A. (2021). A conceptual management model of virtual project team in international companies. Vilnius University Open Series, 6168. https://doi.org/10.15388/vgisc.2021.8 google scholar
33. Laufs D, Peters M, Schultz C (2022) Data platforms for open life sciences-A systematic analysis of management instruments. PLoS ONE 17(10): e0276204. https://doi.org/10.1371/journal.pone.0276204 google scholar
34. Lewis, S., Ellis, J., & Kellogg, W. (2010). Using virtual interactions to explore leadership and collaboration in globally distributed teams.. https://doi.org/10.1145/1841853.1841856 google scholar
35. Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023). Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy and AI, 11, 100208. google scholar
36. Li, X., Li, J., Li, H., Fu, L., Fu, Y., Li, B., . & Jiao, B. (2011). Aquaculture industry in china: current state, challenges, and outlook. Reviews in Fisheries Science, 19(3), 187-200. google scholar
37. Lu, W., Liu, S., Yang, Y., Fu, R., Xiang, X., Qu, Y., & Huang, H. (2015). Design for the emergency command information system architecture of ocean oil spill. Aquatic Procedia, 3, 41-49. google scholar
38. Lu, X., Wijayaratna, K., Huang, Y., & Qiu, A. (2022). Ai-enabled opportunities and transformation challenges for smes in the post-pandemic era: a review and research agenda. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.88506 google scholar
39. Lv, T., Tang, P., & Zhang, J. (2023). A real-time ais data cleaning and indicator analysis algorithm based on stream computing. Scientific Programming, 2023, 1-12. https://doi.org/10.1155/2023/8345603 google scholar
40. Mac Coombea, P. N., Pasanena, J., Petersa, C., Sharmana, C., & Taylora, P. (2017). Senaps: A platform for integrating time-series with modelling systems. In Proceedings of the 22nd International Congress on Modelling and Simulation (MODSIM 2017), Hobart, Tasmania, Australia (pp. 438-444). google scholar
41. Malic, M., Dobrilovic, D., Malic, D., & Stojanov, Z. (2019). Approach in the development of lightweight microservice architecture for small data center monitoring system. International Journal of Electrical Engineering and Computing, 3(2), 61-69. google scholar
42. Malik, Shaveta, Tapas Kumar, Sahoo, A.K., ‘‘Image processing techniques foridentification of fish disease.” In 2017 IEEE 2nd International Conference onSignal and Image Processing (ICSIP), pp. 55-59. IEEE, 2017. google scholar
43. Muzumdar, P., Bhosale, A., Basyal, G. P., & Kurian, G. (2024). Navigating the Docker Ecosystem: A Comprehensive Taxonomy and Survey. arXiv preprint arXiv:2403.17940. google scholar
44. Mia, M. J., Mahmud, R. B., Sadad, M. S., Al Asad, H., & Hossain, R. (2022). An in-depth automated approach for fish disease recognition. Journal of King Saud University-Computer and Information Sciences, 34(9), 7174-7183. google scholar
45. Montani, S. and Striani, M. (2019). Artificial intelligence in clinical decision support: a focused literature survey. Yearbook of Medical Informatics, 28(01), 120-127. https://doi.org/10.1055/s-0039-1677911 google scholar
46. Mueller, J., Hutter, K., Fueller, J., & Matzler, K. (2010). Virtual worlds as knowledge management platform - a practice-perspective. Information Systems Journal, 21(6), 479-501. https://doi.org/10.1111/ j.1365-2575.2010.00366.x google scholar
47. Mustafa, S., Shaleh, S., Shapawi, R., Estim, A., Ching, F., Ibrahim, A., . & Japar, B. (2021). Application of fourth industrial revolution technologies to marine aquaculture for future food: imperatives, challenges and prospects. Sustainable Marine Structures, 3(1), 22-31. https://doi.org/10.36956/sms.v3i1.378 google scholar
48. Nankervis, L., Cobcroft, J., Nguyen, N., & Rimmer, M. (2021). Advances in practical feed formulation and adoption for hybrid grouper (epinephelus fuscoguttatusŞ x e. lanceolatus^) aquaculture. Reviews in Aquaculture, 14(1), 288-307. https://doi.org/10.1111/raq.12 google scholar
49. Nawaz, M., Khan, S., Hussain, S., & Iqbal, J. (2022). A study on application programming interface recommendation: state-of-the-art techniques, challenges and future directions. Library Hi Tech, 41(2), 355-385. https://doi.org/10.1108/lht-02-2022-0103598 google scholar
50. Ottinger, M., Bachofer, F., Huth, J., & Kuenzer, C. (2021). Mapping aquaculture ponds for the coastal zone of asia with sentinel-1 and sentinel-2 time series. Remote Sensing, 14(1), 153. https://doi. org/10.3390/rs14010153 google scholar
51. Ottinger, M., Clauss, K., & Kuenzer, C. (2018). Opportunities and challenges for the estimation of aquaculture production based on earth observation data. Remote Sensing, 10(7), 1076. https://doi. org/10.3390/rs10071076 google scholar
52. Pridgeon, J. and Klesius, P. (2012). Major bacterial diseases in aquaculture and their vaccine development.. Cab Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources, 1-16. https://doi.org/10.1079/pavsnnr20127048 google scholar
53. Rodrîguez, C., Bâez, M., Daniel, F., Casati, F., Trabucco, J., Canali, L., ... & Percannella, G. (2016). Rest apis: a large-scale analysis of compliance with principles and best practices., 21-39. https://doi.org/10.1007/978-3-319-38791-8_2 google scholar
54. Rosenberg, L. (2023). The metaverse and conversational ai as a threat vector for targeted influence.. https://doi.org/10.1109/ccwc57344.2023.10099167 google scholar
55. Saboor, A., Hassan, M. F., Akbar, R., Shah, S. N. M., Hassan, F., Magsi, S. A., & Siddiqui, M. A. (2022). Containerized microservices orchestration and provisioning in cloud computing: A conceptual framework and future perspectives. Applied Sciences, 12(12), 5793. google scholar
56. Shwartz-Asher, D. and Ahituv, N. (2019). Comparison between face-to-face teams and virtual teams with respect to compliance with directives. Journal of Service Science and Management, 12(04), 549571. https://doi.org/10.4236/jssm.2019.12438 google scholar
57. Soklaski, R., Goodwin, J., Brown, O., Yee, M., & Jason, M. (2022). Tools and practices for responsible ai engineering. https://doi. org/10.48550/arxiv.2201.05647 google scholar
58. Song, A. (2020). Artificial Intelligence Technology in Intelligent Farm. Scholar Publishing Group International Journal of Multimedia Computing Vol. 1, Issue 3: 16-28 https://doi.org/10.38007/ IJMC.2020.010302 google scholar
59. Sultan, H., Rashid, W., Shi, J., Rahim, I., Nafees, M., Bohnett, E., ... & Ariza-Montes, A. (2022). Horizon scan of transboundary concerns impacting snow leopard landscapes in asia. Land, 11(2), 248. https:// doi.org/10.3390/land11020248 google scholar
60. Subasinghe, R., Soto, D., & Jia, J. (2009). Global aquaculture and its role in sustainable development. Reviews in Aquaculture, 1(1), 2-9. https://doi.org/10.1111/j.1753-5131.2008.01002.x google scholar
61. Troell, M., Naylor, R., Metian, M., Beveridge, M., Tyedmers, P., Folke, C., . & Zeeuw, A. (2014). Does aquaculture add resilience to the global food system?. Proceedings of the National Academy of Sciences, 111(37), 13257-13263. https://doi.org/10.1073/pnas.1404067111 google scholar
62. Ubina, N. and Cheng, S. (2022). A review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones, 6(1), 12. https://doi.org/10.3390/drones6010012 google scholar
63. Ubina, N., Cheng, S., Chen, H., Chang, C., & Lan, H. (2021). A visual aquaculture system using a cloud-based autonomous drones. Drones, 5(4), 109. https://doi.org/10.3390/drones5040109 google scholar
64. Vo, T., Ko, H., & Kim, Y. (2021). Overview of smart aquaculture system: focusing on applications of machine learning and computer vision. Electronics, 10(22), 2882. https://doi.org/10.3390/electronics10222882 google scholar
65. Wang, J., Yang, X., Wang, Z., & Ge, D. (2022). Monitoring marine aquaculture and implications for marine spatial planning—an example from shandong province, china. Remote Sensing, 14(3), 732. https://doi.org/10.3390/rs14030732 google scholar
66. Wang, Y. (2023). Synergy in silicon: the evolution and potential of academia-industry collaboration in ai and software engineering. https://doi.org/10.36227/techrxiv.23961540 google scholar
67. Watterson, A., Jeebhay, M. F., Neis, B., Mitchell, R., & Cavalli, L. (2020). The neglected millions: the global state of aquaculture workers’ occupational safety, health and well-being. Occupational and environmental medicine, 77(1), 15-18. google scholar
68. Xiang, L., Zhang, Z., Zuo, D., & Yang, X. (2013). Multi-layered system robustness testing strategy based on abnormal parameter. Journal of Computers, 8(7). https://doi.org/10.4304/jcp.8.7.1882-1891 google scholar
69. Watterson, A., Jeebhay, M., Neis, B., Mitchell, R., & Cavalli, L. (2019). The neglected millions: the global state of aquaculture workers’ occupational safety, health and well-being. Occupational and Environmental Medicine, 77(1), 15-18. https://doi.org/10.1136/ oemed-2019-105753 google scholar
70. Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2020). Deep learning for smart fish farming: applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66-90. https://doi. org/10.1111/raq.12464 google scholar
71. Zhang, Q., Lin, J., Wei, W., & Wei, Y. (2022). Evolutionary path and influences on marine ecological farming: dual perspective of government intervention and enterprise participation. Discrete Dynamics in Nature and Society, 2022, 1-12. https://doi. org/10.1155/2022/3250863 google scholar
72. Zheng, W., Lan, Y., Zhang, W., Ouyang, L., & Wen, D. (2023). D >k >i: data-knowledge-driven group intelligence framework for smart service in education metaverse. Ieee Transactions on Systems Man and Cybernetics Systems, 53(4), 2056-2061. https://doi.org/10.1109/ tsmc.2022.3228849 google scholar
73. Zhuang, Y., Wu, F., Chen, C., & Pan, Y. (2017). Challenges and opportunities: from big data to knowledge in ai 2.0. Frontiers of Information Technology & Electronic Engineering, 18(1), 3-14. https:// doi.org/10.1631/fitee.1601883 google scholar
74. Zhou, X., Zhao, X., Zhang, S., & Lin, J. (2019). Marine ranching construction and management in East China Sea: Programs for sustainable fishery and aquaculture. Water, 11(6), 1237. google scholar
75. Zhou, C., Wong, K., Tsou, J., & Zhang, Y. (2022). Detection and statistics of offshore aquaculture rafts in coastal waters. Journal of Marine Science and Engineering, 10(6), 781. https://doi.org/10.3390/ jmse10060781 google scholar
76. Zhou, L., Tan, S., Ahmad, A., & Low, S. (2021). High-flux strategy for electrospun nanofibers in membrane distillation to treat aquaculture wastewater: a review. Journal of Chemical Technology & Biotechnology, 96(12), 3259-3272. https://doi.org/10.1002/jctb.6828 google scholar