[Abangan A.S., Kopp D., Faillettaz R. (2023). Artificial Intelligence for fish behavior recognition may unlock fishing gear selectivity. Front. Mar. Sci., 10.3389/fmars.2023.1010761.]Search in Google Scholar
[Abbas E.M., Al-Souti A.S., Sharawy Z.Z., El-Haroun E., Ashour M. (2023). Impact of dietary administration of seaweed polysaccha-ride on growth, microbial abundance, and growth and immune-related genes expression of the Pacific whiteleg shrimp (Litopenaeus vannamei). Life, 13: 344.]Search in Google Scholar
[Abdelrahman H.A., Hemstreet W.G., Roy L.A., Hanson T.R., Beck B.H., Kelly A.M. (2023). Epidemiology and economic impact of disease-related losses on commercial catfish farms: A seven-year case study from Alabama, USA. Aquaculture, 566: 739206.]Search in Google Scholar
[Adegboye M.A., Aibinu A.M., Kolo J.G., Folorunso T.A., Aliyu I., Lee S.H. (2020). Incorporating intelligent fish feeding regime system using vibration analysis. World J. Wirel. Devic. Engin., 8: 91948–91960.]Search in Google Scholar
[Agossou B.E., Toshiro T. (2021). IoT & AI based system for fish farming: case study of Benin. In: Proceedings of the Conference on Information Technology for Social Good, Roma, Italy. ACM, 259–264.]Search in Google Scholar
[Ahmadzadeh E., Kim H., Jeong O., Kim N., Moon I. (2022). A deep bidirectional LSTM-GRU network model for automated ciphertext classification, IEEE Access., 10: 3228–3237.]Search in Google Scholar
[Ahmed M.S., Aurpa T.T., Azad M.A.K. (2022). Fish disease detection using image based machine learning technique in aquaculture. J. King Saud. Univ. Comput. Inf. Sci., 34: 5170–5182.]Search in Google Scholar
[Ahmedt-Aristizabal D., Armin M.A., Denman S., Fookes C., Peters-son L. (2021). Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors, 21: 4758.]Search in Google Scholar
[Aljehani F., N’Doye I., Laleg-Kirati T.M. (2023). Feeding control and water quality monitoring in aquaculture systems: Opportunities and challenges. arXiv preprint arXiv:2306.09920.]Search in Google Scholar
[Al-Mutairi A.W., Al-Aubidy K.M. (2022). IoT-based smart monitoring and management system for fish farming. Bulle Elec. Eng. Info., 12: 1435–1446.]Search in Google Scholar
[Alshater H., Moemen Y.S., El-Sayed I.E.T. (2023). The impact of Artificial Intelligence on waste management for climate change. The power of data: driving climate change with data science and artificial intelligence innovations. Cham: Springer Nature Switzerland, pp. 39–59.]Search in Google Scholar
[Amogi B.R., Ranjan R., Khot L.R. (2022). November 03–05. Reliable image processing algorithm for sunburn management in green apples. Proc. IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 186–190. https://doi.org/10.1109/MetroAgriFor55389.2022.9964902.]Search in Google Scholar
[An D., Hao J., Wei Y., Wang Y., Yu X. (2021). Application of computer vision in fish intelligent feeding system – A review. Aquac Res., 52: 423–437.]Search in Google Scholar
[Ansari F.A., Guldhe A., Gupta S.K., Rawat I., Bux F. (2021). Improving the feasibility of aquaculture feed by using microalgae. Environ Sci. Pollut Res., 28: 43234–43257.]Search in Google Scholar
[Antonucci F., Costa C. (2020). Precision aquaculture: a short review on engineering inno-vations, Aquacult. Int., 28: 41–57.]Search in Google Scholar
[Arepalli P.G., Akula M., Kalli R.S., Kolli A., Popuri V.P. (2022). Water quality prediction for salmon fish using gated recurrent unit (GRU) model. Proc. 2nd Int. Conf. on Computer Science, Engineering and Applications (ICCSEA), Gunupur, Odisha, India, pp. 1–5.]Search in Google Scholar
[Aryai V., Abbassi R., Abdussamie N., Salehi F., Garaniya V., Asadnia M., Xu C.M. (2021). Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia. Proc. Saf. Environ. Protect., 148: 437–461.]Search in Google Scholar
[Ashraf R.M., Ahmad I., Shah A., Hajam Y.A., Amin A., Khursheed S., Ahmad I., Rasoo S. (2024). Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand. Food Chemist. X, 22: 101309.]Search in Google Scholar
[Austin B., Lawrence A., Can E., Carboni C., Crockett J., Demirtas N., Schleder D., Adolfo J., Kayis S., Karacalar U., Kizak V., Kop A., Thompson K., Ruiz C.A.M., Serdar O., Seyhaneyildiz Can S., Watts S., Yucel G.G. (2022). Selected topics in sustainable aqua-culture research: Current and future focus. Sustain Aquat Res., 1: 74–125.]Search in Google Scholar
[Awad A., Mohammady E.Y., Souady M.R., Rabetimarghezar N., El-Haroun E.R., Hassaan M.S. (2024). Growth and physiological response of Nile tilapia (Oreochromis niloticus) fed a fermented mixture of plant protein sources. Anim. Feed Sci. Technol., 315: 116034.]Search in Google Scholar
[Barber I. (2007). Parasites, behaviour and welfare in fish. Appl. Anim. Behav. Sci., 104: 251–264.]Search in Google Scholar
[Barreto M.O., Rey P.S., Yang Y., Phillips C., Descovich K. (2022). Emerging indicators of fish welfare in aquaculture. Rev Aquac., 14: 343–361.]Search in Google Scholar
[Bilodeau S.M., Schwartz A.W.H., Xu B., Pauca V.P., Silman M.R. (2022). A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis. PloS One, 17-e0263377.]Search in Google Scholar
[Bochkovskiy A., Wang C., Liao H.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv, 10934: 4–23.]Search in Google Scholar
[Carleton K.L., Escobar-Camacho D., Stieb S.M., Cortesi F., Justin Marshall N. (2020). Seeing the rainbow: Mechanisms underlying spectral sensitivity in teleost fishes. J. Exp. Biol., 223.]Search in Google Scholar
[Chahid A., N’Doye I., Majoris J.E., Berumen M.L., Laleg-Kirati T.-M. (2022). Fish growth trajectory tracking using Q-learning in precision aquaculture. Aquaculture, 550: 737838.]Search in Google Scholar
[Chen F., Sun Y., Du J., Xu L., Zhou T., Qiu J.S. (2022 a). Intelligent feeding technique based on predicting shrimp growth in re-circulating aquaculture system Aquacult. Res., 53: 4401–4413.]Search in Google Scholar
[Chen J.C., ChenT.L., Wang H.L., Chang P.C. (2022 b). Underwater abnormal classification system based on deep learning: a case study on aquaculture fish farm in Taiwan. Aquac. Eng., 99: 102290.]Search in Google Scholar
[Chen T., Lv L., Wang D., Zhang J., Yang Y., Zhao Z., Tao D. (2023). Revolutionizing agrifood systems with Artificial Intelligence: a survey. arXiv preprint, arXiv:2305.01899]Search in Google Scholar
[Chiu M.C., Yan W.M., Bhat S.A., Huang N.F. (2022). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. J. Agricult. Food Res., 9: 100357.]Search in Google Scholar
[Christensen J.H., Mogensen L.V., Galeazzi R., Andersen J.C. (2018). Detection, localization and classification of fish and fish species in poor conditions using convolutional neural networks; detection, localization and classification of fish and fish species in poor conditions using convolutional neural networks. IEEE, 1–6.]Search in Google Scholar
[Craig S.R., Helfrich L.A., Kuhn D., Schwarz M.H. (2017). Understanding fish nutrition, feeds, and feeding. Virginia Cooperative Extension, Publication 420–256.]Search in Google Scholar
[Cuende E., Herrmann B., Sistiaga M., Basterretxea M., Edridge A., Mackenzie E.K. (2022). Species separation efficiency and effect of artificial lights with a horizontal grid in the Basque bottom trawl fishery. Ocean Coast. Manag., 221: 106105.]Search in Google Scholar
[Daniels R.R., Taylor R.S., Robledo D., Macqueen D.J. (2023). Single cell genomics as a transformative approach for aquaculture research and innovation. Rev. Aquac., 15: 1618–1637.]Search in Google Scholar
[Darapaneni N., Sreekanth S., Paduri A.R., Roche A.S., Murugappan V., Singha K.K., Shenwai A.V. (2022). AI based farm fish disease detection system to help micro and small fish farmers. Proc. 2022 Interdiscipl. Res. in Technol and Manage., 1–5.]Search in Google Scholar
[Dawkins M., Sherrill L., Fieldhouse K., Hoogs A., Richards B., Zhang D. (2017). An open-source platform for underwater image & video analytics. Proc. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA, Institute of Electrical and Electronics Engineers Inc., pp. 898–906.]Search in Google Scholar
[Dellosa R.M. (2023). Bangus (Chanos chanos) farming: Preparing for SMART farming and predictive analysis using Artificial Intelligence Tools. Int. J. Intel. Syst. Applic. Engin.,11: 665–672.]Search in Google Scholar
[Dey K., Shekhawat U. (2021). Blockchain for sustainable e-agriculture: Literature review, architecture for data management, and implications. J. Clean Prod., 316: 128254.]Search in Google Scholar
[Duangwongsa J., Ungsethaphand T., Akaboot P., Khamjai S., Unankard S. (2021). Real-time water quality monitoring and notification system for aquaculture. Proc. 2021 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunication engineering. IEEE, 9–13.]Search in Google Scholar
[Durden J.M., Schoening T., Althaus F., Friedman A., Garcia R., Glover A.G. (2016). Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding. Oceanogr. Mar. Biol.: Annu. Rev., 54: 315–366.]Search in Google Scholar
[El Basuini M.F., Zaki M.A.A., El-Hais Abdelaziz M., Elhanafy M.G., El-Bilawy E.H., Zaineldin A.I., Abdel-Aziz M.F.A., Abouelsaad I.A., El-Ratel I.T., Mzengereza K., Shadrack R.S., Teiba I.I. (2024). Microbial, immune and antioxidant responses of Nile tilapia with dietary nano-curcumin supplements under chronic low temperatures. Aquacult. Fish., 9: 57–65.]Search in Google Scholar
[Emam W., El-Rewiny M.N., Abou Zaid A.A., El-Tras W.F., Mohamed R.A. (2022). Trends in the use of feed and water additives in Egyptian tilapia culture. Aquacult. Rese., 53: 3331–3336. Er-Rousse O., Qafas A. (2024). Artificial Intelligence for the optimization of marine aquaculture. E3S Web of Conferences, 477.]Search in Google Scholar
[Essa M.A., Helal A.M., Abou Shabana N.M., Abdelaty B.S., Elokaby M.A., El Hammamy M., Baroma M., Aboseif A.M., Zaher M.M., Ashour M., Main K.L. (2024). The effectiveness of coded wire-tagging and release methods as an approach for increasing the stocking of grey mullet (Mugil cephalus) in an Egyptian enclosed Wadi El-Rayan Lake. Eg. J. Aquat. Biol. Fish., 28: 717–734.]Search in Google Scholar
[FAO (2022). The State of World Fisheries and Aquaculture: Towards Blue Transformation. Food and Agriculture Organization of the United Nations.]Search in Google Scholar
[FAO (2018). The State of World Fisheries and Aquaculture 2018 – Overview of aquaculture Artificial Intelligence (AAI) applications Meeting the Sustainable Development Goals. http://www.fao.org/documents/card/en/c/I9540EN/.]Search in Google Scholar
[Flinn S.A., Midway S.R. (2021). Trends in growth modeling in fisheries science. Fishes, 6: 1–18.]Search in Google Scholar
[Føre M., Alver M., Alfredsen J.A., Marafioti G., Senneset G., Birkevold J., Willumsen F.V., Lange G., Espmark A., Terjesen B.F. (2016). Modelling growth performance and feeding behaviour of Atlantic salmon (Salmo salar L.) in commercial-size aquaculture net pens: Model details and validation through full-scale experiments. Aquaculture, 464: 268–278.]Search in Google Scholar
[Føre M., Frank K., Norton T., Svendsen E., Alfredsen J.A., Dempster T., Eguiraun H., Watson W., Stahl A., Sunde L.M., Schellewald C., Skøien K.R., Alver M.O., Berck-Mans D. (2018). Precision fish farming: a new framework to improve production in aquaculture. Biosyst. Eng., 173: 176–193.]Search in Google Scholar
[Føre M., Alver M.O., Frank K., Alfredsen J.A. (2023). Chapter 9: Advanced technology in aquaculture – Smart feeding in marine fish farms. In: Smart Livestock Nutrition. Springer, pp. 227–268.]Search in Google Scholar
[Gasco L., Acuti G., Bani P., Dalle Zotte A., Danieli P.P., De Angelis A., Fortina R., Marino R., Parisi G., Piccolo G., Pinotti L., Prandini A., Schiavone A., Terova G., Tulli F., Roncarati A. (2020). Insect and fish by-products as sustainable alternatives to conventional animal proteins in animal nutrition. It. J. Anim. Sci., 19: 360–372. ]Search in Google Scholar
[Georgopoulos V.P., Gkikas D.C., Theodorou J.A. (2023). Factors influencing the adoption of Artificial Intelligence technologies in agriculture, livestock farming and aquaculture: a systematic literature review using PRISMA 2020. Sustainability, 15: 16385.]Search in Google Scholar
[Gladju J., Kamalam B.S., Kanagaraj A. (2022). Applications of data mining and machine learning framework in aquaculture and fish-eries: A review. Smart Agricult. Technol., 2: 100061.]Search in Google Scholar
[Glencross B., Fracalossi D.M., Hua K., Izquierdo M., Mai K., Øverland M., Yakupityage A. (2023). Harvesting the benefits of nutritional research to address global challenges in the 21st century. J. World Aquacult. Soc., 54: 343–363.]Search in Google Scholar
[Glenn J. (2020). YOLOv5 Documentation. Available at: https://docs.ultralytics.com/ (Accessed on: March 10, 2022).]Search in Google Scholar
[Goda A.A.S., Srour T.M., Omar E., Mansour A.T., Baromh M.Z., Mohamed S.A., Davies S.J., El-Haroun E.R. (2019). Appraisal of a high protein distiller’s dried grain (DDG) in diets for European sea bass, Dicentrarchus labrax fingerlings on growth performance, haematological status and related gut histology. Aquacult. Nutr., 25: 808–816.]Search in Google Scholar
[Goda A.M.A.S., Ahmed S.R., Nazmi H.M., Baromh M.Z., Kevin F., Waldemar R.J., Simon D., El-Haroun E.R. (2020 a). Partial replacement of dietary soybean meal by high-protein distiller’s dried grains (HPDDG) supplemented with protease enzyme for European sea bass, Dicentrarchus labrax fingerlings. Aquacult. Nutr., 26: 842–852.]Search in Google Scholar
[Goda A.M.A.S., Sherine R.A., Nazmi H.M., Ahmad M.A., Mostafa K.S.T., Susan H.F., Baromh Z.M., El-Haroun E.R., Simon D. (2020 b). Assessment of a high protein distillers dried grain (HPDDG) augmented with phytase in diets for European sea bass, Dicentrarchus labrax fingerlings on growth performance, haematological status, immune response and related gut and liver histology. Aquaculture, 529: 735617.]Search in Google Scholar
[Goda A.M.A.S., Aboseif A.M., Mohammed E.Y., Taha M.K.S., Man-sour A.A., Ramadan E.A., Aboushabana N.A., Zaher M.M., Otazua N.I., Ashour M. (2024). Earthen pond-based floating beds for rice-fish co-culture as a novel concept for climate adaptation, water efficiency improvement, nitrogen and phosphorus management. Aquaculture, 579: 740215.]Search in Google Scholar
[Hassan S.A.H., Sharawy Z.Z., Hemeda S.A., El Nahas A.F., El-Haroun E., Doan H.V., Davies S.J., Abbas E.M. (2024). Sugarcane bagasse ameliorates zootechnical performances and expression of growth-immune-related, and antioxidant genes of Litopenaeus vannamei larvae in the biofloc technology. Aquacult. Rep., 35: 102015.]Search in Google Scholar
[He P. (2010). Behavior of marine fishes: capture processes and conservation challenges. Wiley-Blackwell, Iowa.]Search in Google Scholar
[Helal A.M., Zaher M.M., Meshhal D.T., Ashour M., Younis E.M., Abdelwarith A.A., Al-Afify A.D.G., Sharawy Z.Z., Davies S., El-Haroun E., Nassif M.G. (2024). Biofloc supplementation improves growth performances, nutrient utilization, and histological status of Nile tilapia (Oreochromis niloticus) while enhancing zooplankton diversity, community, and abundance. Aquaculture, 585: 740711.]Search in Google Scholar
[Hu H., Tang C., Shi C., Qian Y. (2023). Detection of residual feed in aquaculture using YOLO and Mask RCNN. Aquac. Eng., 100. Hu W.C., Chen L.B, Member S., Huang B.K., Lin H.M. (2022). A computer vision-based intelligent fish feeding system using deep learning techniques for aquaculture. IEEE Sens J., 22: 7.]Search in Google Scholar
[Hu W.C., Liang-Bi C., Bo-Hao W., Guo-Wei L., Xiang-Rui H. (2024). Design and implementation of a full-time Artificial Intelligence of things-based water quality inspection and prediction system for intelligent aquaculture. IEEE Sens J., 24: 3811–3821.]Search in Google Scholar
[Hu Z., Li R., Xia X., Yu C., Fan X., Zhao Y. (2020). A method overview in smart aquaculture. Environ. Monit. Assess, 192: 1–25.]Search in Google Scholar
[Jahanbakht M., Xiang W., Hanzo L., Azghadi M.R. (2021). Internet of underwater things and big marine data analytics – a comprehensive survey. IEEE Commun. Surveys Tutorials., 23: 904–956.]Search in Google Scholar
[Jalal A., Salman A., Mian A., Shortis M., Shafait F. (2020). Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol. Inform., 57: 101088.]Search in Google Scholar
[Jawad H.M., Nordin R., Gharghan S.K., Jawad A.M., Ismail M. (2017). Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors, 17: 1781.]Search in Google Scholar
[Jie C., Yingying S., Junhui W., Yusheng W., Huiping S., Kaiyan L. (2019). Intelligent control and management system for recirculating aquaculture. Proc. 2nd International Conference on Electronics and Communication Engineering, ICECE, pp. 438–443.]Search in Google Scholar
[Jung J.-S., Shin D.H. (2023). Factors affecting intention to accept Artificial Intelligence-based smart aquaculture system. Proc. 4th South American International Industrial Engineering and Operations Management Conference, Lima, Peru, pp. 9–11.]Search in Google Scholar
[Kaur I., Behl T., Aleya L., Rahman H., Kumar A., Arora S., Bulbul I.J. (2021). Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic. Environ. Sci. Pollut. Res. Int., 28: 40515–40532.]Search in Google Scholar
[Kristmundsson J., Patursson Ø., Potter J., Xin Q. (2023). Fish monitoring in aquaculture using multibeam echosounders and machine learning. IEEE Access, 11: 108306–108316.]Search in Google Scholar
[Kumar Y., Koul A., Singla R., Ijaz M.F. (2022). Artificial Intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J. Ambient Intell. Human. Comput., pp. 1–28.]Search in Google Scholar
[Leonard W.K L. (2024). Implementation of Artificial Intelligence in aquaculture and fisheries: deep learning, machine vision, big data, internet of things, robots and beyond. J. Comput. Cognit. Engin., 3: 112–118.]Search in Google Scholar
[Li D., Wang Z., Wu S., Miao Z., Du L., Duan Y. (2020). Automatic recognition methods of fish feeding behavior in aquaculture: a review. Aquaculture, 735508.]Search in Google Scholar
[Li D., Li X., Wang Q., Hao Y. (2022). Advanced techniques for the intelligent diagnosis of fish diseases: a review. Animals, 12: 2938.]Search in Google Scholar
[Li D., Du Z., Qi W., Wang J., Du L. (2024). Recent advances in acoustic technology for aquaculture: A review. Rev. Aquacult., 10: 449–515.]Search in Google Scholar
[Li J., Xu W., Deng L., Xiao Y., Han Z., Zheng H. (2023) Deep learning for visual recognition and detection of aquatic animals: A review. Rev. Aquac., 15: 409–433.]Search in Google Scholar
[Lim L.W.K. (2024). Implementation of Artificial Intelligence in aqua-culture and fisheries: deep learning, machine vision, big data, internet of things, robots and beyond. J. Computat Cognit. Engin., 3: 112–118.]Search in Google Scholar
[Lin Y., Yu K., Hao L., Wang J., Bu J. (2022). An indoor Wi-Fi localization algorithm using ranging model constructed with transformed RSSI and BP neural network. IEEE Transact. Commun., 70: 2163–2177.]Search in Google Scholar
[Logares R., Alos J., Catalan I., Solana A.C., Javier del Ocampo F. (2021). Oceans of big data and Artificial Intelligence. Oceans. CSIC Scientific Challenges Towards 2030, pp. 163–179.]Search in Google Scholar
[López-Cortés X.A., Nachtigall F.M., Olate V.R., Araya M., Oyanedel S., Dia V., Jakob E., Ríos-Momberg M., Santos L.S. (2017). Fast detection of pathogens in salmon farming industry. Aquaculture, 470: 17–24.]Search in Google Scholar
[Lotfy A.M., Elhetawy A.I.G., Habiba M.M., Sherine R.A., Helal A.M., Abdel-Rahim M.M. (2023). Growth, feed utilization, blood biochemical variables, immunity, histology of the intestine, gills and liver tissues, and carcass composition of the European sea-bass (Dicentrarchus labrax) raised using different water sources. Egypt J. Aquac. Biol. Fish, 27: 687–711.]Search in Google Scholar
[Luna M., Llorente I., Cobo A. (2019). Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms. Ann. Oper. Res., 1–26.]Search in Google Scholar
[Machuve D., Nwankwo E., Mduma N., Mbelwa J. (2022). Poultry diseases diagnostics models using deep learning. Front. Artif Intell., 5: 733345.]Search in Google Scholar
[MacIntyre C.R., Chen X., Kunasekaran M., Quigley A., Lim S., Stone H., Gurdasani D. (2023). Artificial Intelligence in public health: the potential of epidemic early warning systems. J. Int. Med. Res., 51: 03000605231159335.]Search in Google Scholar
[Mandal A., Ghosh A.R. (2023). AI-driven surveillance of the health and disease status of ocean organisms: a review. Aquac. Int., 192: 1–12.]Search in Google Scholar
[Mandal A., Ghosh A.R. (2024). Role of Artificial Intelligence (AI) in fish growth and health status monitoring: a review on sustainable aquaculture. Aquacult. Internat., 32: 2791–2820.]Search in Google Scholar
[Mao J., Xiao G., Sheng W., Qu Z., LiuY. (2016). Research on realizing the 3D occlusion tracking location method of fish’s school target. Neurocomputing, 214: 61–79.]Search in Google Scholar
[Mao J., Xiao G., Sheng W., Hayat T., Liu X. (2017). A theoretical 2D image model for locating 3D targets. Int. J. Comput. Math., 94: 1430–1450.]Search in Google Scholar
[Mayormente M.D. (2024). Intelligent recirculating aquaculture system of Oreochromis niloticus: a feed-conversion-ratio-based machine learning approach. Int. J. Intel. Syst. Appl. Eng., 12: 122–128.]Search in Google Scholar
[Migaud H., Bell G., Cabrita E., McAndrew B., Davie A., Bobe J., Herraez M.P., Carrillo M. (2013). Gamete quality and broodstock management in temperate fish. Rev. Aquacult., 5: 194–223.]Search in Google Scholar
[Mijwil M.M., Adelaja O., Badr A ., Ali G., Buruga B.A., Pudasaini P. (2023). Innovative livestock: A survey of Artificial Intelligence techniques in livestock farming management. Wasit J. Comp. Math. Sci., 2: 99–106.]Search in Google Scholar
[Moustahfid H., Michaels W., Alger B., Gangopadhyay A., Brehmer P. (2020). Advances in fisheries science through emerging observing technologies. Proc. Global Oceans 2020: Singapore – U.S. Gulf Coast, Biloxi, MS, USA.]Search in Google Scholar
[Muñoz-Benavent P., Martínez-Peiró J., Andreu-García G., Puig-Pons V., Espinosa V., Pérez-Arjona I., Ortega A. (2022). Impact evaluation of deep learning on image segmentation for automatic bluefin tuna sizing. Aquac. Eng., 99: 102299.]Search in Google Scholar
[Mustapha U.F., Alhassan A.W., Jiang D.N., Li G.L. (2021). Sustainable aquaculture development: a review on the roles of Cloud computing, Internet of Things and Artificial Intelligence (CIA). Rev Aquac., 13: 2076–2091.]Search in Google Scholar
[Nayan A.A., Saha J., Mozumder A.N., Mahmud K.R., Al Azad A.K., Kibria M.G. (2021). A machine learning approach for early detection of fish diseases by analyzing water quality. Trends Sci., 18: 351.]Search in Google Scholar
[Negreiros M.M., Yamashita S., Sardenberg T., Favero E.L.J., Ribeiro F.A., Haddad W.T.J., Haddad V.J. (2016). Diagnostic imaging of injuries caused by venomous and traumatogenic catfish. Rev. Soc. Bras. Med. Trop., 49: 530–533.]Search in Google Scholar
[O’Donncha F., Grant J. (2019). Precision aquaculture. In: IEEE Internet of Things Magazine, 2: 26–30.]Search in Google Scholar
[Olanubi O.O., Akano T.T., Asaolu O.S. (2024). Design and development of an IoT-based intelligent water quality management system for aquaculture. J. Electr. Syst. Inf. Technol., 11: 15.]Search in Google Scholar
[Panda R.K., Baral D. (2023). Adoption of AI/ML in aquaculture: a study on pisciculture. J. Surv. Fish Sci., pp. 228–233.]Search in Google Scholar
[Park J.S., Oh M.J., Han S. (2007). Fish disease diagnosis system based on image processing of pathogens microscopic images. Proc. Conference on Frontiers in the Convergence of Bioscience and Information Technologies, Jeju, Korea, pp. 878–883.]Search in Google Scholar
[Peebles W.S., Zhu J., Zhang R., Torralba A., Efros A.A., Shechtman E. (2022). GAN-supervised dense visual alignment. Proc. IEEE/ CVF Conference on Computer Vision and Pattern Recognition, CVPR2022, New Orleans, LA, USA, 24: 13460–13471.]Search in Google Scholar
[Peng D., He D., Li Y., Wang Z. (2022). Integrating terrestrial and satellite multibeam systems toward 6G: Techniques and challenges for interference mitigation. IEEE Wireless Commun., 29: 24–31.]Search in Google Scholar
[Portz D.E., Woodley C.M., Cech J.J. (2006). Stress-associated impacts of short-term holding on fishes. Rev. Fish Biol. Fisher., 16: 125–170.]Search in Google Scholar
[Prem R., Tewari V.K. (2020). Development of human-powered fish feeding machine for freshwater aquaculture farms of developing countries. Aquac. Eng., 88: 102028.]Search in Google Scholar
[Quach L.D., Pham-Quoc N., Tran D.C., Fadzil Hassan M. (2020). Identification of chicken diseases using VGGNet and ResNet models. Proc. 6th EAI International Conference, INISCOM 2020, 27–28.08.2020, Hanoi, Vietnam, Industrial Networks and Intelligent Systems, pp. 259–269.]Search in Google Scholar
[Ragab S., Hoseinifar S.H., Van Doan H., El-Haroun E. (2024). Evaluation of distiller’s dried grains with solubles in aquafeeds: a review. Ann. Anim. Sci., 24: 65–75.]Search in Google Scholar
[Raj A., Jayanthi J. (2018). IoT-based real-time poultry monitoring and health status identification. Proc. 11th IEEE, pp. 1–7.]Search in Google Scholar
[Rajaee T., Khani S., Ravansalar M. (2020). Artificial Intelligence-based single and hybrid models for prediction of water quality in rivers: a review. Chemomet. Intelligent Lab. Syst., 200: 103978.]Search in Google Scholar
[Ranjan R., Khot L.R., Peters R.T., Salazar-Gutierrez M.R., Shi G. (2020). In-field crop physiology sensing aided real-time apple fruit surface temperature monitoring for sunburn prediction. Comput. Electron. Agric., 175: 105558.]Search in Google Scholar
[Ranjan R., Sharrer K., Tsukuda S., Good C. (2023). MortCam: an Artificial Intelligence-aided fish mortality detection and alert system for recirculating aquaculture. Aquacult. Engin., 102: 102341.]Search in Google Scholar
[Robert M., Cortay A., Morfin M., Simon J., MorandeauF., Deneubourg J.L. (2020). A methodological framework for characterizing fish swimming and escapement behaviors in trawls. PloS One, 15:0243311.]Search in Google Scholar
[Romli M.A., Daud S., Raof R.A.A., Ahmad Z.A., Mahrom N. (2018). Aquaponic growbed water level control using fog architecture. J. Phys. Malásia, 1018.]Search in Google Scholar
[Saberioon M., Gholizadeh A., Cisar P., Pautsina A., Urban J. (2017). Application of ma-chine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquacult., 9: 369–387.]Search in Google Scholar
[Sailaja N., Surapaneni R.K., Lakshmi R.J., Venkateswarlu D. (2023). A study in analysing key factors in adopting Artificial Intelligence in revamping supply chain systems in aqua field. J. Surv. Fish. Sci., 10: 2157–2162.]Search in Google Scholar
[Sankaran S., Khot L.R., Espinoza C.Z., Jarolmasjed S., Sathuvalli V.R., Vandemark G. J., Pavek M.J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron., 70: 112–123.]Search in Google Scholar
[Sherine R.A., Hassaan M., Fitzsimmons K., El-Haroun E. (2023). Chapter 8. Alternative protein sources for sustainable tilapia farming. In: Novel approaches toward sustainable tilapia aquaculture. Springer, pp. 201–227.]Search in Google Scholar
[Sherine R.A., Hoseinifar S.H., Doan H.V., El-Haroun E. (2024). Evaluation of distillers dried grains with solubles in aquafeeds – a review. Ann. Anim. Sci., 24: 65–75.]Search in Google Scholar
[Shreesha S., Pai M.M.M., Pai R.M., Verma U. (2023). Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture. Ecol. Infor., 78: 102287.]Search in Google Scholar
[Singh A., Gupta H., Srivastava A., Srivastava A., Joshi R.C., Dutta M.K. (2021). A novel pilot study on imaging-based identification of fish exposed to heavy metal (Hg) contamination. J. Food Process. Preserv., 45: e15571.]Search in Google Scholar
[Singh M., Sahoo K.S., Gandomi A.H. (2024). An intelligent-IoT-based data analytics for freshwater recirculating aquaculture system. IEEE J., 11: 4206–4217.]Search in Google Scholar
[Stasko A.D., Gunn J.M., Johnston T.A. (2012). Role of ambient light in structuring north-temperate fish communities: Potential effects of increasing dissolved organic carbon concentration with a changing climate. Environ. Rev., 20: 173–190.]Search in Google Scholar
[Su J., Chen J., Wen J., XieW., Lin M. (2020). Analysis decision-making system for aquaculture water quality based on deep learning. J. Physics: Conf. Ser., 1544: 012028.]Search in Google Scholar
[Sun M., Hassan S.G., Li D. (2016). Models for estimating feed intake in aquaculture: a review. Comp. Elect. Agricult., 127: 425–438.]Search in Google Scholar
[Sung-Hyun K., Seongak O.H., Sangwon L.E.E. (2023). Designing dataset for Artificial Intelligence learning for cold sea fish farming. Int. J. Advan. Smart Converg., 12: 2288–2847.]Search in Google Scholar
[Svenning M.A., Falkegård M., Dempson J.B., Power M., Bårdsen B.J., Guðbergsson G., Fauchald P. (2022). Temporal changes in the relative abundance of anadromous Arctic charr, brown trout, and Atlantic salmon in northern Europe: Do they reflect changing climates? Freshwater Biol., 67: 64–77.]Search in Google Scholar
[Taleb H.M., Mahrose K., Abdel-Halim A.A., Kasem H., Ramadan G.S., Fouad A.M., Khafaga A.F., Khalifa N.E., Kamal M., Salem H.M., Alqhtani A.H., Swelum A.A., Arczewska-Włosek A., Świątkiewicz S., Abd El-Hack M.E. (2025). Using artificial intelligence to improve poultry productivity – a review. Ann. Anim. Sci., 25: 23–33.]Search in Google Scholar
[Tiyasha T., Tung T.M., Bhagat S.K., Tan M.L., Jawad A.H., Mohtar W.H.M.W. (2021). Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: development of hybrid tree-based artificial intelligence models. Mar. Pollut. Bull., 170: 112639.]Search in Google Scholar
[Tonachella T., Martini A., Martinoli M., Pulcini D., Romano A., Capoccioni F. (2022). An affordable and easy to use tool for automatic fish length and weight estimation in mariculture. Sci. Rep., 12: 15642.]Search in Google Scholar
[Torres A., Abril A.M., Clua E.E.G. (2020). A time-extended (24 h) baited remote underwater video (BRUV) for monitoring pelagic and nocturnal marine species. J. Mar. Sci. Eng., 8: 208.]Search in Google Scholar
[Tsai K.L., Chen L.W., Yang L.J., Shiu H.J., Chen H.W. (2022). IoT based smart aquaculture system with automatic aerating and water quality monitoring. J. Int. Technol., 23: 177–184.]Search in Google Scholar
[Tsolakis N., Schumacher R., Dora M., Kumar M. (2022). Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation? Ann. Oper. Res., 1–54.]Search in Google Scholar
[Ubina N.A., Lan H.-Y., Cheng S.-C., Chang C.-C., Lin S.-S., Zhang K.-X., Lu H.-Y., Cheng C.-Y., Hsieh Y.-Z. (2023). Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT). Smart Agricult. Technol., 5: 100285.]Search in Google Scholar
[Ullah I., Kim D. (2018). An optimization scheme for water pump control in smart fish farm with efficient energy consumption. Processes, 6: 65.]Search in Google Scholar
[Underwood M.J., Utne Palm A.C., Øvredal J.T., Bjordal A. (2021). The response of mesopelagic organisms to artificial lights. Aqua-cult. Fish., 6: 519–529.]Search in Google Scholar
[Verdal H.D., Komen H., Quillet E., Chatain B., Allal F., Benzie J.A., Vandeputte M. (2018). Improving feed efficiency in fish using selective breeding: a review. Aquaculture, 10: 833–851.]Search in Google Scholar
[Vo T.T.E., Ko H., Huh J.-H., Kim Y. (2021). Overview of smart aqua-culture system: Focusing on applications of machine learning and computer vision. Electronics, 10: 2882.]Search in Google Scholar
[Walsh D.P., Ma T.F., Ip H.S., Zhu J. (2019). Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses. Transbound. Emerg. Dis., 66: 2537–2545.]Search in Google Scholar
[Wang C., Li Z., Wang T., Xu X., Zhang X ., Li D. (2021). Intelligent fish farm – the future of aquaculture. Aquac. Int., 29: 2681–2711.]Search in Google Scholar
[Wang H., Zhang S., Zhao S., Wang Q., Li D., Zhao R. (2022 a). Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. Comput. Electron. Agric., 192: 106512.]Search in Google Scholar
[Wang Q., Du Z., Jiang G., Cui M., Li D., Liu C., Li W. (2022 b). A real-time individual identification method for swimming fish based on improved Yolov5. Available at SSRN: https://ssrn.com/abstract=4044575 or http://dx.doi.org/10.2139/ssrn.4044575]Search in Google Scholar
[Xia M., Chen X., Lang H., Shao H., Williams D., Gazivoda M. (2022). Features and always-on wake-up detectors for sparse acoustic event detection. Electronics, 11: 478.]Search in Google Scholar
[Xia S., Yao Z., Li Y., Mao S. (2021). Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT. IEEE, 20: 6743–6757.]Search in Google Scholar
[Xu J., Sang W., Dai H., Lin C., Ke S., Mao J. (2022). A detailed analysis of the effect of different environmental factors on fish photo-tactic behavior: Directional fish guiding and expelling technique. Animals: Open Access J. from MDPI, [online] 12: 240.]Search in Google Scholar
[Yamuna R., Harsharani K.S., Manasa S.M., Sathya M., Lenish P., Kumari A. (2023). IWQMA: Intelligent Water Quality Management in Aquaculture using IoT Technology. J. Homepage, 18: 183–198.]Search in Google Scholar
[Yang H., Sun M., Liu S. (2023 a). A hybrid intelligence model for predicting dissolved oxygen in aquaculture water. Front. Mar. Sci., 10: 1126556.]Search in Google Scholar
[Yang P.Y., Liao Y.C., Chou F.I. (2023 b). Artificial Intelligence in Internet of Things system for predicting water quality in aquaculture fishponds. CSSE, 46: 3.]Search in Google Scholar
[Yasin E.T., Ozkan I.A., Koklu M. (2023). Detection of fish freshness using artificial intelligence methods. Europ. Food Res. Technol., 249: 1979–1990.]Search in Google Scholar
[Yu W., Xu X., Jin S., Ma Y., Liu B., Gong W. (2022). BP neural network retrieval for remote sensing atmospheric profile of ground-based microwave radiometer, IEEE Geosci. Remote. Sens. Lett., 19: 1–5.]Search in Google Scholar
[Yu X., Wang Y., An D., Wei Y. (2021). Identification methodology of special behaviors for fish school based on spatial behavior characteristics. Comp. Electron Agric., 185.]Search in Google Scholar
[Zaki M.A.A., Alabssawy A.N., Nour A.A.M., El Basuini M.F., Da-wood M.A.O., Alkahtani S., Abdel-Daim M.M. (2020). The impact of stocking density and dietary carbon sources on the growth, oxidative status and stress markers of Nile tilapia (Oreochromis niloticus) reared under biofloc conditions. Aquacult. Rep., 16: 8.]Search in Google Scholar
[Zhan F., Yu Y., Wu R., Zhang J., Lu S., Zhang C. (2022). Marginal contrastive correspondence for guided image generation In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10653–10662.]Search in Google Scholar
[Zhang L., Wang J., Li B., Liu Y., Zhang H., Duan Q. (2022). A MobileNetV2-SENet-based method for identifying fish school feeding behavior. Aquac. Eng., 99: 102288.]Search in Google Scholar
[Zhang L., Li B., Sun X., Hong Q., Duan Q. (2023). Intelligent fish feeding based on machine vision: A review. Biosyst. Eng., 231: 133–164.]Search in Google Scholar
[Zhao S., Zhang S., Liu J., Wang H., Zhu J., Li D., Zhao R. (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 540: 115: 736724.]Search in Google Scholar
[Zhou C., Xu D., Lin K., Sun C., Yang X. (2018). Intelligent feeding control methods in aquaculture with an emphasis on fish: a review. Rev. Aquacult., 10: 975–993.]Search in Google Scholar
[Zhou Y., Yang J., Tolba A., Alqahtani F., Qi X., Shen Y. (2023). A data-driven intelligent management scheme for digital industrial aqua-culture based on multi-object deep neural network. Math. Biosci. Eng., 20: 10428–10443.]Search in Google Scholar
[Zhou Z., Dong X., Li Z., Yu K., Ding C., Yang Y. (2022). Spatio-temporal feature encoding for traffic accident detection in VANET environment. IEEE Transact. Intell. Transport. Syst., 23: 19772–19781.]Search in Google Scholar
[Zounemat-Kermani M., Seo Y., Kim S., Ghorbani M.A., Samadianfard S., Naghshara S., Kim N.W., Singh V.P. (2019). Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Appl. Sci. -Basel, 9.]Search in Google Scholar
