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However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities.<\/jats:p>","DOI":"10.3390\/foods15183205","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:04:57Z","timestamp":1789045497000},"page":"3205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Artificial Intelligence in Aquaculture, Processing, Safety, and Traceability in the Industry of Aquatic Products: A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6227-0565","authenticated-orcid":false,"given":"Jingshu","family":"Chen","sequence":"first","affiliation":[{"name":"College of Food Science and Engineering, Tarim University, Alar 843301, China"},{"name":"Production & Construction Group Key Laboratory of Special Agricultural Products Further Processing in Southern Xinjiang, Alar 843301, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengtao","family":"Ji","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Agri-Product Quality and Safety Traceability, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Agri-Product Quality and Safety Traceability, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1049-6815","authenticated-orcid":false,"given":"Hongbing","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Animal and Food Sciences, University of Kentucky, Lexington, KY 40546, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueyue","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering, Tarim University, Alar 843301, China"},{"name":"Production & Construction Group Key Laboratory of Special Agricultural Products Further Processing in Southern Xinjiang, Alar 843301, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ce","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Food Science and Engineering, Tarim University, Alar 843301, China"},{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"School of Food and Health, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.48130\/FIA-2023-0009","article-title":"Marine Proteins and Peptides: Production, Biological Activities, and Potential Applications","volume":"2","author":"Fernando","year":"2023","journal-title":"Food Innov. Adv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, Y., Duan, Y., Wei, Y., An, D., and Liu, J. (2022). Application of Intelligent and Unmanned Equipment in Aquaculture: A Review. Comput. Electron. Agric., 199.","DOI":"10.1016\/j.compag.2022.107201"},{"key":"ref_3","first-page":"508","article-title":"AI-Driven Aquaculture: A Review of Technological Innovations and Their Sustainable Impacts","volume":"15","author":"Yang","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.aaf.2021.04.009","article-title":"An Overview of Disruptive Technologies for Aquaculture","volume":"7","author":"Yue","year":"2022","journal-title":"Aquac. Fish."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rashvand, M., Ren, Y., Sun, D.-W., Senge, J., Krupitzer, C., Fadiji, T., Mir\u00f3, M.S., Shenfield, A., Watson, N.J., and Zhang, H. (2025). Artificial Intelligence for Prediction of Shelf-Life of Various Food Products: Recent Advances and Ongoing Challenges. Trends Food Sci. Technol., 159.","DOI":"10.1016\/j.tifs.2025.104989"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Russo, G.L., Langellotti, A.L., Torrieri, E., and Masi, P. (2024). Emerging Technologies in Seafood Processing: An Overview of Innovations Reshaping the Aquatic Food Industry. Compr. Rev. Food Sci. Food Saf., 23.","DOI":"10.1111\/1541-4337.13281"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, W., Ouyang, Z., Zhang, Y., Lu, Y., Wei, C., Tu, Y., and He, B. (2025). Research Progress on the Artificial Intelligence Applications in Food Safety and Quality Management. Trends Food Sci. Technol., 156.","DOI":"10.1016\/j.tifs.2024.104855"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1111\/1541-4337.13110","article-title":"Recent Advances in Shelf Life Prediction Models for Monitoring Food Quality","volume":"22","author":"Cui","year":"2023","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, G., Xiao, K., and Chen, M. (2019). An Intelligent IoT-Based Control and Traceability System to Forecast and Maintain Water Quality in Freshwater Fish Farms. Comput. Electron. Agric., 166.","DOI":"10.1016\/j.compag.2019.105013"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yu, X., Liu, N., Liu, C., Jiang, A., and Chen, L. (2025). Integrating AI with Detection Methods, IoT, and Blockchain to Achieve Food Authenticity and Traceability from Farm-to-Table. Trends Food Sci. Technol., 158.","DOI":"10.1016\/j.tifs.2025.104925"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1111\/raq.12559","article-title":"Sustainable Aquaculture Development: A Review on the Roles of Cloud Computing, Internet of Things and Artificial Intelligence (CIA)","volume":"13","author":"Mustapha","year":"2021","journal-title":"Rev. Aquac."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Quaade, S., Vallebueno, A., Alcabes, O.D.N., Rodolfa, K.T., and Ho, D.E. (2024). Remote Sensing and Computer Vision for Marine Aquaculture. Sci. Adv., 10.","DOI":"10.1126\/sciadv.adn4944"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/raq.12820","article-title":"A Systematic Review of Drying in Aquatic Products","volume":"16","author":"Zeng","year":"2024","journal-title":"Rev. Aquac."},{"key":"ref_14","first-page":"421","article-title":"Fuzzy PID Control System Optimization and Verification for Oxygen-Supplying Management in Live Fish Waterless Transportation","volume":"11","author":"Zhang","year":"2024","journal-title":"Inf. Process. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zhu, M., Ding, W., Zhao, S., and Gu, J. (2020). Feed Requirement Determination of Grass Carp (Ctenopharyngodon idella) Using a Hybrid Method of Bioenergetics Factorial Model and Fuzzy Logic Control Technology under Outdoor Pond Culturing Systems. Aquaculture, 521.","DOI":"10.1016\/j.aquaculture.2020.734970"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pallottino, F., Violino, S., Figorilli, S., Pane, C., Aguzzi, J., Colle, G., Nerio Nemmi, E., Montaghi, A., Chatzievangelou, D., and Antonucci, F. (2025). Applications and Perspectives of Generative Artificial Intelligence in Agriculture. Comput. Electron. Agric., 230.","DOI":"10.1016\/j.compag.2025.109919"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1016\/j.scib.2025.02.014","article-title":"Advancing Tele-Perception: A Paradigm Shift from Traditional Non-Contact Sensing to Adaptive Embodied Artificial Intelligence Systems","volume":"70","author":"Du","year":"2025","journal-title":"Sci. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, W., Du, Z., Xu, X., Bai, Z., Han, J., Cui, M., and Li, D. (2024). A Review of Aquaculture: From Single Modality Analysis to Multimodality Fusion. Comput. Electron. Agric., 226.","DOI":"10.1016\/j.compag.2024.109367"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, G., Yu, J., Xu, W., Muhammad, A., and Li, D. (2025). Automated Fish Counting System Based on Instance Segmentation in Aquaculture. Expert Syst. Appl., 259.","DOI":"10.1016\/j.eswa.2024.125318"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1111\/raq.12218","article-title":"Intelligent Feeding Control Methods in Aquaculture with an Emphasis on Fish: A Review","volume":"10","author":"Zhou","year":"2018","journal-title":"Rev. Aquac."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.foodres.2014.03.012","article-title":"Principles, Developments and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A Review","volume":"62","author":"Zhang","year":"2014","journal-title":"Food Res. Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compag.2012.07.008","article-title":"Fish Species Classification by Color, Texture and Multi-Class Support Vector Machine Using Computer Vision","volume":"88","author":"Hu","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.compag.2016.10.009","article-title":"Vision-Based Discrimination of Tuna Individuals in Grow-out Cages through a Fish Bending Model","volume":"130","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.aquaculture.2018.04.064","article-title":"Modified Motion Influence Map and Recurrent Neural Network-Based Monitoring of the Local Unusual Behaviors for Fish School in Intensive Aquaculture","volume":"493","author":"Zhao","year":"2018","journal-title":"Aquaculture"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/raq.12842","article-title":"Recent Advances in Acoustic Technology for Aquaculture: A Review","volume":"16","author":"Li","year":"2024","journal-title":"Rev. Aquac."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1111\/raq.12388","article-title":"Nonintrusive Methods for Biomass Estimation in Aquaculture with Emphasis on Fish: A Review","volume":"12","author":"Li","year":"2020","journal-title":"Rev. Aquac."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2776","DOI":"10.1109\/TMC.2020.3044397","article-title":"Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: A Test Case for Pelagic Fish","volume":"21","author":"Testolin","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1111\/2041-210X.12330","article-title":"Measuring Acoustic Habitats","volume":"6","author":"Merchant","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hassan, W., F\u00f8re, M., Ulvund, J.B., and Alfredsen, J.A. (2019). Internet of Fish: Integration of Acoustic Telemetry with LPWAN for Efficient Real-Time Monitoring of Fish in Marine Farms. Comput. Electron. Agric., 163.","DOI":"10.1016\/j.compag.2019.06.005"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S0044-8486(03)00074-7","article-title":"An Acoustic Detector of Turbot Feeding Activity","volume":"221","author":"Mallekh","year":"2003","journal-title":"Aquaculture"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Su, X., Sutarlie, L., and Loh, X.J. (2020). Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality. Research, 2020.","DOI":"10.34133\/2020\/8272705"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tang, X., Jiang, H., Wen, R., Xue, D., Zeng, W., Han, Y., and Wu, L. (2024). Advancements and Challenges on SERS-Based Multimodal Biosensors for Biotoxin Detection. Trends Food Sci. Technol., 152.","DOI":"10.1016\/j.tifs.2024.104672"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wei, T., Shi, Y., and Sun, J. (2025). A Multimodal Interaction-Based Approach to Situational Driving Anger Emotion Regulation. Expert Syst. Appl., 274.","DOI":"10.1016\/j.eswa.2025.127051"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Qiu, L., Yang, Z., Chen, Y., and Zhang, Y. (2025). MGF-GCN: Multimodal Interaction Mamba-Aided Graph Convolutional Fusion Network for Semantic Segmentation of Remote Sensing Images. Inf. Fusion, 122.","DOI":"10.1016\/j.inffus.2025.103150"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., and Chanussot, J. (2022). Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review. Int. J. Appl. Earth Obs. Geoinf., 112.","DOI":"10.1016\/j.jag.2022.102926"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.inffus.2020.07.002","article-title":"Remote Sensing Image Classification Using Subspace Sensor Fusion","volume":"64","author":"Rasti","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"84283","DOI":"10.1109\/ACCESS.2023.3296098","article-title":"A Comparative Analysis of Early and Late Fusion for the Multimodal Two-Class Problem","volume":"11","author":"Pereira","year":"2023","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s00138-021-01249-8","article-title":"Early, Intermediate and Late Fusion Strategies for Robust Deep Learning-Based Multimodal Action Recognition","volume":"32","author":"Boulahia","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1109\/TCSVT.2022.3208833","article-title":"RGB-T Semantic Segmentation with Location, Activation, and Sharpening","volume":"33","author":"Li","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiao, J., Wei, Y., Jie, Z., Shi, H., Lau, R., and Huang, T.S. (2019). Geometry-Aware Distillation for Indoor Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00298"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1111\/1365-2478.13200","article-title":"Automated Well Log Depth Matching: Late Fusion Multimodal Deep Learning","volume":"72","author":"Duffaut","year":"2024","journal-title":"Geophys. Prospect."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gu, X., Zhao, S., Duan, Y., Meng, Y., Li, D., and Zhao, R. (2025). MMFINet: A Multimodal Fusion Network for Accurate Fish Feeding Intensity Assessment in Recirculating Aquaculture Systems. Comput. Electron. Agric., 232.","DOI":"10.1016\/j.compag.2025.110138"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.biosystemseng.2024.07.008","article-title":"Non-Destructive Detection of Sturgeon Breath under Waterless Low Temperature Stress Using Microenvironment and Breath Angle Multi-Modal Sensing","volume":"245","author":"Zhang","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mohammadi, E., Rahimian, M., and Panahi, B. (2025). Bridging the Gap: Phage Manufacturing Processes from Laboratory to Agri-Food Industry. Virus Res., 353.","DOI":"10.1016\/j.virusres.2025.199537"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s11273-018-9600-4","article-title":"Time Series Forecasting Model for Fisheries in Chilika Lagoon (a Ramsar Site, 1981), Odisha, India: A Case Study","volume":"26","author":"Raman","year":"2018","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"\u00c7ak\u0131r, M., Oral, O., Ural, G.N., and Y\u0131lmaz, M. (2026). Mapping the Research Landscape of Machine Learning in Aquaculture: A Bibliometric Analysis with Forecasted Output by 2030. Aquaculture, 612.","DOI":"10.1016\/j.aquaculture.2025.743258"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"You, J., Hao, G., Gan, X., Chen, R., Chen, Y., Zhang, Z., Sun, A., Liu, H., and Shi, X. (2024). Extreme Gradient Boosting-Enhanced Molecularly Imprinted Fluorescence Nanosensor for Rapid Identification and Visual Detection of Deltamethrin in Seawater and Aquatic Products. Sens. Actuators B Chem., 405.","DOI":"10.1016\/j.snb.2024.135355"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.3390\/agriengineering6020070","article-title":"Optimizing Convolutional Neural Networks, XGBoost, and Hybrid CNN-XGBoost for Precise Red Tilapia (Oreochromis Niloticus Linn.) Weight Estimation in River Cage Culture with Aerial Imagery","volume":"6","author":"Taparhudee","year":"2024","journal-title":"AgriEngineering"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1038\/s41529-025-00673-9","article-title":"Advancing LightGBM with Data Augmentation for Predicting the Residual Strength of Corroded Pipelines","volume":"9","author":"Wang","year":"2025","journal-title":"npj Mater. Degrad."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Siddique, M.A.B., Mahalder, B., Haque, M.M., Shohan, M.H., Biswas, J.C., Akhtar, S., and Ahammad, A.K.S. (2024). Forecasting of Tilapia (Oreochromis Niloticus) Production in Bangladesh Using ARIMA Model. Heliyon, 10.","DOI":"10.1016\/j.heliyon.2024.e27111"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dong, X., Xu, Z., Zhao, H., Wu, D., Qu, B., Liu, S., and Xiao, B. (2025). Predictive Modeling and Interpretability Analysis of Bioconcentration Factors for Organic Chemicals in Fish Using Machine Learning. Environ. Pollut., 377.","DOI":"10.1016\/j.envpol.2025.126323"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Baez-Villanueva, O.M., Zambrano-Bigiarini, M., Beck, H.E., McNamara, I., Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J.D., and Xuan Thinh, N. (2020). RF-MEP: A Novel Random Forest Method for Merging Gridded Precipitation Products and Ground-Based Measurements. Remote Sens. Environ., 239.","DOI":"10.1016\/j.rse.2019.111606"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Saville, R., Fujiwara, A., Hatanaka, K., Wada, M., Yaman, A., Puspasari, R., Albasri, H., and Dwiyoga, N. (2026). AI-Powered Decision Support System for Mariculture: Real-Time Fish Mortality Prediction with Random Forest. Aquac. Eng., 112.","DOI":"10.1016\/j.aquaeng.2025.102621"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, Y., Chen, T., Gu, X., Zhang, L., Li, X., Tang, R., He, Y., Chen, G., and Zhang, B. (2024). Monitoring Water Quality Parameters of Freshwater Aquaculture Ponds Using UAV-Based Multispectral Images. Ecol. Indic., 167.","DOI":"10.1016\/j.ecolind.2024.112644"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Massaro, A., Stella, R., Negro, A., Bragolusi, M., Miano, B., Arcangeli, G., Biancotto, G., Piro, R., and Tata, A. (2021). New Strategies for the Differentiation of Fresh and Frozen\/Thawed Fish: A Rapid and Accurate Non-Targeted Method by Ambient Mass Spectrometry and Data Fusion (Part a). Food Control, 130.","DOI":"10.1016\/j.foodcont.2021.108364"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Duan, Y., Li, X., Zhang, L., Chen, D., Liu, S., and Ji, H. (2020). Mapping National-Scale Aquaculture Ponds Based on the Google Earth Engine in the Chinese Coastal Zone. Aquaculture, 520.","DOI":"10.1016\/j.aquaculture.2019.734666"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Kanwal, S., Abdullah, M., Kumar, S., Arshad, S., Shahroz, M., Zhang, D., and Kumar, D. (2024). An Optimal Internet of Things-Driven Intelligent Decision-Making System for Real-Time Fishpond Water Quality Monitoring and Species Survival. Sensors, 24.","DOI":"10.3390\/s24237842"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.tifs.2020.06.022","article-title":"Expert-Driven Methodology to Assess and Predict the Effects of Drivers of Change on Vulnerabilities in a Food Supply Chain: Aquaculture of Atlantic Salmon in Norway as a Showcase","volume":"103","author":"Marvin","year":"2020","journal-title":"Trends Food Sci. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Qian, L., Yu, G., Liu, H., and He, Z. (2025). Segmentation and Calculation of Splashes Area during Fish Feeding Using Deep Learning and Linear Regression. Comput. Electron. Agric., 234.","DOI":"10.1016\/j.compag.2025.110208"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gkikas, M.C., Gkikas, D.C., Vonitsanos, G., Theodorou, J.A., and Sioutas, S. (2024). Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark. Appl. Sci., 14.","DOI":"10.3390\/app142210112"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lara-Ben\u00edtez, P., Carranza-Garc\u00eda, M., and Riquelme, J.C. (2021). An Experimental Review on Deep Learning Architectures for Time Series Forecasting. Int. J. Neural Syst., 31.","DOI":"10.1142\/S0129065721300011"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhou, X., Hao, Y., Liu, Y., Dang, L., Qiao, B., and Zuo, X. (2025). Short-Term Prediction of Dissolved Oxygen and Water Temperature Using Deep Learning with Dual Proportional-Integral-Derivative Error Corrector in Pond Culture. Eng. Appl. Artif. Intell., 142.","DOI":"10.1016\/j.engappai.2024.109964"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Q., Song, L., and Chen, Y. (2019). Attention-Based Recurrent Neural Networks for Accurate Short-Term and Long-Term Dissolved Oxygen Prediction. Comput. Electron. Agric., 165.","DOI":"10.1016\/j.compag.2019.104964"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Dewi, T., Mardiyati, E.N., Risma, P., and Oktarina, Y. (2025). Hybrid Machine Learning Models for PV Output Prediction: Harnessing Random Forest and LSTM-RNN for Sustainable Energy Management in Aquaponic System. Energy Convers. Manag., 330.","DOI":"10.1016\/j.enconman.2025.119663"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s44443-025-00045-y","article-title":"A Novel Transformer-Based Dual Attention Architecture for the Prediction of Financial Time Series","volume":"37","author":"Hadizadeh","year":"2025","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zheng, K., Yang, R., Li, R., Guo, P., Yang, L., and Qin, H. (2023). A Spatiotemporal Attention Network-Based Analysis of Golden Pompano School Feeding Behavior in an Aquaculture Vessel. Comput. Electron. Agric., 205.","DOI":"10.1016\/j.compag.2022.107610"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Cui, M., Zhao, J., Mei, X., Li, D., and Zeng, L. (2026). Dual-Branch Mutual Learning for Multi-Modal to Single-Modal Fish Feeding Intensity Recognition in Aquaculture. Biosyst. Eng., 267.","DOI":"10.1016\/j.biosystemseng.2026.104468"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hasan, M., Vasker, N., Hossain, M.M., Bhuiyan, M.I., Biswas, J., and Ahmmad Rashid, M.R. (2024). Framework for Fish Freshness Detection and Rotten Fish Removal in Bangladesh Using Mask R\u2013CNN Method with Robotic Arm and Fisheye Analysis. J. Agric. Food Res., 16.","DOI":"10.1016\/j.jafr.2024.101139"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, L., He, Y., Zhang, X., and Liu, X. (2023). Intelligent Pulsed Ultraviolet c Radiation Sterilization System: A Cleaner Solution of Raw Ready-to-Eat Aquatic Products Processing. J. Clean. Prod., 427.","DOI":"10.1016\/j.jclepro.2023.139281"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0144-8609(00)00044-3","article-title":"Process Control and Artificial Intelligence Software for Aquaculture","volume":"23","author":"Lee","year":"2000","journal-title":"Aquac. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.fss.2015.05.009","article-title":"Fuzzy Logic\u2014A Personal Perspective","volume":"281","author":"Zadeh","year":"2015","journal-title":"Fuzzy Sets Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s12711-024-00944-0","article-title":"Sex Identification in Rainbow Trout Using Genomic Information and Machine Learning","volume":"56","author":"Kudinov","year":"2024","journal-title":"Genet. Sel. Evol."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, B., Yang, J., Fan, Y., Qian, X., and Li, H. (2021). Magnetic Properties and Its Application in the Prediction of Potentially Toxic Elements in Aquatic Products by Machine Learning. Sci. Total Environ., 783.","DOI":"10.1016\/j.scitotenv.2021.147083"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Z., Cao, L., Sui, J., Lin, H., Wang, X., and Wang, K. (2025). Machine Learning-Assisted Early Evaluation of Aquatic Products Freshness Based on UV-Enhanced Multi-Enzymatic Cascade. Microchem. J., 211.","DOI":"10.1016\/j.microc.2025.113117"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Y\u00f1iguez, A.T., and Ottong, Z.J. (2020). Predicting Fish Kills and Toxic Blooms in an Intensive Mariculture Site in the Philippines Using a Machine Learning Model. Sci. Total Environ., 707.","DOI":"10.1016\/j.scitotenv.2019.136173"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.compag.2018.05.025","article-title":"Automated within Tank Fish Mass Estimation Using Infrared Reflection System","volume":"150","author":"Saberioon","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Han, Q., Lu, J., Zhu, J., Lin, L., Zheng, Z., and Jiang, S. (2025). Non-Destructive Detection of Freshness in Crayfish (Procambarus clarkii) Based on near-Infrared Spectroscopy Combined with Deep Learning. Food Control, 168.","DOI":"10.1016\/j.foodcont.2024.110858"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.biosystemseng.2023.03.003","article-title":"Dynamic and Explainable Fish Mortality Prediction under Low-Concentration Ammonia Nitrogen Stress","volume":"228","author":"Wu","year":"2023","journal-title":"Biosyst. Eng."},{"key":"ref_80","first-page":"261","article-title":"Feed Intake Prediction Model for Group Fish Using the MEA-BP Neural Network in Intensive Aquaculture","volume":"7","author":"Chen","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, J., and Duan, Q. (2020). Estimation for Fish Mass Using Image Analysis and Neural Network. Comput. Electron. Agric., 173.","DOI":"10.1016\/j.compag.2020.105439"},{"key":"ref_82","first-page":"185","article-title":"Prediction of Dissolved Oxygen in a Fishery Pond Based on Gated Recurrent Unit (GRU)","volume":"8","author":"Li","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Shao, K., Li, D., Tang, H., Zhang, Y., Xu, B., and Bhatti, U.A. (2025). Improving Multi-Step Dissolved Oxygen Prediction in Aquaculture Using Adaptive Temporal Convolution and Optimized Transformer. Comput. Electron. Agric., 235.","DOI":"10.1016\/j.compag.2025.110329"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Mahmood, F., Govindan, R., Bermak, A., Yang, D., Khadra, C., and Al-Ansari, T. (2021). Energy Utilization Assessment of a Semi-Closed Greenhouse Using Data-Driven Model Predictive Control. J. Clean. Prod., 324.","DOI":"10.1016\/j.jclepro.2021.129172"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"107820","DOI":"10.1016\/j.compag.2023.107820","article-title":"Closed-Loop Operation of a Simulated Recirculating Aquaculture System with an Integrated Application of Nonlinear Model Predictive Control and Moving Horizon Estimation","volume":"209","author":"Kamali","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1053\/j.gastro.2025.03.038","article-title":"Generative Artificial Intelligence in Clinical Medicine and Impact on Gastroenterology","volume":"169","author":"Soroush","year":"2025","journal-title":"Gastroenterology"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Fini, C., Amato, S.G., Scutaru, D., Biancardi, S., Antonucci, F., Violino, S., Ortenzi, L., Nemmi, E.N., Mei, A., and Pallottino, F. (2025). Application of Generative Artificial Intelligence in the Aquacultural Sector. Aquac. Eng., 111.","DOI":"10.1016\/j.aquaeng.2025.102568"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Akram, W., Din, M.U., Saad Saoud, L., and Hussain, I. (2026). A Review of Generative AI in Aquaculture: Applications, Case Studies and Challenges for Smart and Sustainable Farming. Aquac. Eng., 112.","DOI":"10.1016\/j.aquaeng.2025.102637"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"54207","DOI":"10.1109\/ACCESS.2018.2872025","article-title":"Autoencoder-Combined Generative Adversarial Networks for Synthetic Image Data Generation and Detection of Jellyfish Swarm","volume":"6","author":"Kim","year":"2018","journal-title":"IEEE Access"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.ultrasmedbio.2023.11.008","article-title":"Automated Measurement of Ovary Development in Atlantic Salmon Using Deep Learning","volume":"50","author":"Yari","year":"2024","journal-title":"Ultrasound Med. Biol."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Canovi, N., Ellis, B.A., S\u00f8rdalen, T.K., Allken, V., Halvorsen, K.T., Malde, K., and Beyan, C. (2024). Trajectory-Based Fish Event Classification through Pre-Training with Diffusion Models. Ecol. Inform., 82.","DOI":"10.1016\/j.ecoinf.2024.102733"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Treeprapin, K., Bandatang, R., Panthum, T., Singchat, W., Prasanpan, J., Srikulnath, K., and Trirongjitmoah, S. (2025). Enhancing Clariid Catfish Species Classification: A Deep Learning Framework Utilizing Cranial Morphology and Explainable AI. Smart Agric. Technol., 12.","DOI":"10.1016\/j.atech.2025.101165"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Cai, K., Yang, Z., Gao, T., Liang, M., Liu, P., Zhou, S., Pang, H., and Liu, Y. (2024). Efficient Recognition of Fish Feeding Behavior: A Novel Two-Stage Framework Pioneering Intelligent Aquaculture Strategies. Comput. Electron. Agric., 224.","DOI":"10.1016\/j.compag.2024.109129"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.fishres.2019.05.004","article-title":"Validation of Fish Length Estimations from a High Frequency Multi-Beam Sonar (ARIS) and Its Utilisation as a Field-Based Measurement Technique","volume":"218","author":"Cook","year":"2019","journal-title":"Fish. Res."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Adade, S.Y.-S.S., Lin, H., Johnson, N.A.N., Qianqian, S., Nunekpeku, X., Ahmad, W., Kwadzokpui, B.A., Ekumah, J.-N., and Chen, Q. (2024). Rapid Qualitative and Quantitative Analysis of Benzo(b)Fluoranthene (BbF) in Shrimp Using SERS-Based Sensor Coupled with Chemometric Models. Food Chem., 454.","DOI":"10.1016\/j.foodchem.2024.139836"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, C., Wan, R., and Xu, L. (2023). Applying Artificial Intelligence to Optimize the Trawling Path and Operational Parameters for Antarctic Krill. Ocean Eng., 289.","DOI":"10.1016\/j.oceaneng.2023.116231"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Kunimatsu, S., Kurota, H., Muko, S., Ohshimo, S., and Tomiyama, T. (2025). Predicting Unseen Chub Mackerel Densities through Spatiotemporal Machine Learning: Indications of Potential Hyperdepletion in Catch-per-Unit-Effort Due to Fishing Ground Contraction. Ecol. Inform., 85.","DOI":"10.1016\/j.ecoinf.2024.102944"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhang, T., Yang, Y., Liu, Y., Liu, C., Zhao, R., Li, D., and Shi, C. (2024). Fully Automatic System for Fish Biomass Estimation Based on Deep Neural Network. Ecol. Inform., 79.","DOI":"10.1016\/j.ecoinf.2023.102399"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2019.11.002","article-title":"Automatic Measurement of the Body Length of Harvested Fish Using Convolutional Neural Networks","volume":"189","author":"Tseng","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_100","first-page":"2247","article-title":"Machine Vision Based Fish Cutting Point Prediction for Target Weight","volume":"75","author":"Jang","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Zhang, R., Chen, X., Wan, Z., Wang, M., and Xiao, X. (2023). Deep Learning-Based Oyster Packaging System. Appl. Sci., 13.","DOI":"10.3390\/app132413105"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Jia, Z., Li, M., Shi, C., Zhang, J., and Yang, X. (2022). Determination of Salmon Freshness by Computer Vision Based on Eye Color. Food Packag. Shelf Life, 34.","DOI":"10.1016\/j.fpsl.2022.100984"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1080\/10942912.2022.2120000","article-title":"Tilapia Freshness Prediction Utilizing Gas Sensor Array System Combined with Convolutional Neural Network Pattern Recognition Model","volume":"25","author":"Sun","year":"2022","journal-title":"Int. J. Food Prop."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Liu, H., You, J., Liu, C., Zhang, Z., Sun, A., Hao, G., and Shi, X. (2024). Machine Learning-Assisted Wide-Gamut Fluorescence Visual Test Paper for Propazine Determination in Fish and Seawater Samples. Sens. Actuators B Chem., 413.","DOI":"10.1016\/j.snb.2024.135843"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"John, E.P., and Mishra, U. (2024). Integrated Multitrophic Aquaculture Supply Chain Fish Traceability with Blockchain Technology, Valorisation of Fish Waste and Plastic Pollution Reduction by Seaweed Bioplastic: A Study in Tuna Fish Aquaculture Industry. J. Clean. Prod., 434.","DOI":"10.1016\/j.jclepro.2023.140056"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Huang, W., Wang, Y., He, W., and Zhang, X. (2025). RTI-Net: A Decision Support System for Fish Stress Classification Using Multimodal Learning Network. Expert Syst. Appl., 276.","DOI":"10.1016\/j.eswa.2025.127174"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yu, H., Zhang, X., Zhang, P., Tu, W., and Gu, L. (2024). Fish Behavior Recognition Based on an Audio-Visual Multimodal Interactive Fusion Network. Aquac. Eng., 107.","DOI":"10.1016\/j.aquaeng.2024.102471"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.compag.2018.02.006","article-title":"Near Infrared Computer Vision and Neuro-Fuzzy Model-Based Feeding Decision System for Fish in Aquaculture","volume":"146","author":"Zhou","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, Q., Zhang, H., Yang, J., Guo, Z., Bulugu, I., and Shen, Y. (2023). A Deep Vision Sensing-based Fuzzy Control Scheme for Smart Feeding in the Industrial Recirculating Aquaculture Systems. Electron. Lett., 59.","DOI":"10.1049\/ell2.12727"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.biosystemseng.2021.05.019","article-title":"Application of an Adaptive PID Controller Enhanced by a Differential Evolution Algorithm for Precise Control of Dissolved Oxygen in Recirculating Aquaculture Systems","volume":"208","author":"Zhou","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10499-025-01945-6","article-title":"TDA-GLM: Text Data Augmentation for Aquaculture Disease Prevention and Control via a Small Model-Guided ChatGLM","volume":"33","author":"Qiao","year":"2025","journal-title":"Aquac. Int."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"21377","DOI":"10.1038\/s41598-025-05892-3","article-title":"An Integrating RAG-LLM and Deep Q-Network Framework for Intelligent Fish Control Systems","volume":"15","author":"Danvirutai","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.fishres.2018.10.019","article-title":"The Future of Recreational Fisheries: Advances in Science, Monitoring, Management, and Practice","volume":"211","author":"Brownscombe","year":"2019","journal-title":"Fish. Res."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, X., Liu, J., An, D., and Wei, Y. (2022). Dynamic Feeding Method for Aquaculture Fish Using Multi-Task Neural Network. Aquaculture, 551.","DOI":"10.1016\/j.aquaculture.2022.737913"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.cub.2017.12.002","article-title":"Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering","volume":"28","author":"Marques","year":"2018","journal-title":"Curr. Biol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1007\/s11831-020-09486-2","article-title":"Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review","volume":"28","author":"Yang","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yang, X., Zhao, C., and Zhou, C. (2025). TMVF: Trusted Multi-View Fish Behavior Recognition with Correlative Feature and Adaptive Evidence Fusion. Inf. Fusion, 118.","DOI":"10.1016\/j.inffus.2024.102899"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"5170","DOI":"10.1016\/j.jksuci.2021.05.003","article-title":"Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture","volume":"34","author":"Ahmed","year":"2022","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s12601-021-00015-1","article-title":"A Review: Marine Bio-Logging of Animal Behaviour and Ocean Environments","volume":"56","author":"Chung","year":"2021","journal-title":"Ocean Sci. J."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Banan, A., Nasiri, A., and Taheri-Garavand, A. (2020). Deep Learning-Based Appearance Features Extraction for Automated Carp Species Identification. Aquac. Eng., 89.","DOI":"10.1016\/j.aquaeng.2020.102053"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"6208","DOI":"10.1016\/j.jksuci.2021.03.004","article-title":"A Deep Multi-Resolution Approach Using Learned Complex Wavelet Transform for Tuna Classification","volume":"34","author":"Jose","year":"2022","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1002\/lom3.10113","article-title":"Fish Species Classification in Unconstrained Underwater Environments Based on Deep Learning: Fish Classification Based on Deep Learning","volume":"14","author":"Salman","year":"2016","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Liu, C., Wang, Z., Li, Y., Zhang, Z., Li, J., Xu, C., Du, R., Li, D., and Duan, Q. (2023). Research Progress of Computer Vision Technology in Abnormal Fish Detection. Aquac. Eng., 103.","DOI":"10.1016\/j.aquaeng.2023.102350"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Ashraf Rather, M., Ahmad, I., Shah, A., Ahmad Hajam, Y., Amin, A., Khursheed, S., Ahmad, I., and Rasool, S. (2024). Exploring Opportunities of Artificial Intelligence in Aquaculture to Meet Increasing Food Demand. Food Chem. X, 22.","DOI":"10.1016\/j.fochx.2024.101309"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1186\/s13071-025-07124-z","article-title":"A Machine Learning-Driven Early Warning System for Cryptocaryoniasis in Marine Aquaculture","volume":"18","author":"Xie","year":"2025","journal-title":"Parasites Vectors"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Liu, W., Lyu, J., Wu, D., Cao, Y., Ma, Q., Lu, Y., and Zhang, X. (2022). Cutting Techniques in the Fish Industry: A Critical Review. Foods, 11.","DOI":"10.3390\/foods11203206"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, Q., Ma, H., Min, W., Wang, Y., Zhao, R., Zhou, Y., Tan, Y., Luo, Y., and Hong, H. (2024). Recent Advances in Fish Cutting: From Cutting Schemes to Automatic Technologies and Internet of Things Innovations. Compr. Rev. Food Sci. Food Saf., 23.","DOI":"10.1111\/1541-4337.70039"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1093\/af\/vfac012","article-title":"Achieving Robotic Meat Cutting","volume":"12","author":"Khodabandehloo","year":"2022","journal-title":"Anim. Front."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Azarmdel, H., Mohtasebi, S.S., Jafary, A., Behfar, H., and Rosado Mu\u00f1oz, A. (2021). Design and Simulation of a Vision-Based Automatic Trout Fish-Processing Robot. Appl. Sci., 11.","DOI":"10.3390\/app11125602"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Hassoun, A., Boukid, F., Ozogul, F., A\u00eft-Kaddour, A., Soriano, J.M., Lorenzo, J.M., Perestrelo, R., Galanakis, C.M., Bono, G., and Bouyahya, A. (2023). Creating New Opportunities for Sustainable Food Packaging through Dimensions of Industry 4.0: New Insights into the Food Waste Perspective. Trends Food Sci. Technol., 142.","DOI":"10.1016\/j.tifs.2023.104238"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Gupta, A.K., Kumar, A., and Kumar, B. (2025). Advancing Sustainable Food Packaging: Integrating Machine Learning, Deep Learning, and Artificial Intelligence. Trends Food Sci. Technol., 163.","DOI":"10.1016\/j.tifs.2025.105148"},{"key":"ref_132","first-page":"52","article-title":"Development of a Metering Mechanism with Serial Robotic Arm for Handling Paper Pot Seedlings in a Vegetable Transplanter","volume":"5","author":"Paradkar","year":"2021","journal-title":"Artif. Intell. Agric."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Lv, Z., Chen, T., Cai, Z., and Chen, Z. (2023). Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp. Appl. Sci., 13.","DOI":"10.3390\/app131810018"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Jiao, X., Zhu, J., Ye, W., Zou, H., Yan, B., Zhang, N., Qiang, J., Tao, Y., Zhang, H., and Zhang, D. (2025). Artificial Intelligence in Smart Seafood Safety across the Supply Chains: Recent Advances and Future Prospects. Trends Food Sci. Technol., 163.","DOI":"10.1016\/j.tifs.2025.105161"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Moosavi-Nasab, M., Khoshnoudi-Nia, S., Azimifar, Z., and Kamyab, S. (2021). Evaluation of the Total Volatile Basic Nitrogen (TVB-N) Content in Fish Fillets Using Hyperspectral Imaging Coupled with Deep Learning Neural Network and Meta-Analysis. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-84659-y"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Wang, K., Yue, Z., Lin, H., Wang, Q., Wang, L., Tian, Y., and Ren, L. (2023). Rapid Classification of the Freshness Grades of Sea Bass (Lateolabrax japonicus) Fillets Using a Portable Raman Spectrometer with Machine Learning Method. Microchem. J., 192.","DOI":"10.1016\/j.microc.2023.108948"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"405","DOI":"10.48130\/fia-0024-0037","article-title":"Noninvasive Freshness Evaluation of Bighead Carp Heads Based on Fluorescence Spectroscopy Coupled with Long Short-Term Memory Network: Simulation of Cold Chains","volume":"3","author":"You","year":"2024","journal-title":"Food Innov. Adv."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"62484","DOI":"10.1109\/ACCESS.2023.3286980","article-title":"Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms with Hyperparameter Optimization","volume":"11","author":"Wijaya","year":"2023","journal-title":"IEEE Access"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"37445","DOI":"10.1021\/acsami.4c04223","article-title":"Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness","volume":"16","author":"Zhang","year":"2024","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Yu, N., Mao, J., Yang, Q., Jiao, L., Wang, X., Shi, W., Yu, H., and Wei, S. (2023). The Occurrence, Tissue Distribution, and PBT Potential of per- and Polyfluoroalkyl Substances in the Freshwater Organisms from the Yangtze River via Nontarget Analysis. J. Hazard. Mater., 458.","DOI":"10.1016\/j.jhazmat.2023.131868"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Bitts\u00e1nszky, A., Bilicki, V., Sud\u00e1r, G., S\u00fcth, M., Kusza, S., and T\u00f3th, A.J. (2026). Artificial Intelligence in Postharvest Food Safety Control of Animal-Source Foods: Evidence Thresholds, Validation, and Regulatory Applicability. Vet. Sci., 13.","DOI":"10.3390\/vetsci13060574"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1093\/icesjms\/fsz036","article-title":"How Emerging Data Technologies Can Increase Trust and Transparency in Fisheries","volume":"77","author":"Probst","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/s13677-024-00696-8","article-title":"Using Blockchain and AI Technologies for Sustainable, Biodiverse, and Transparent Fisheries of the Future","volume":"13","author":"Alsharabi","year":"2024","journal-title":"J. Cloud Comp."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s10479-022-04785-2","article-title":"Artificial Intelligence and Blockchain Implementation in Supply Chains: A Pathway to Sustainability and Data Monetisation?","volume":"327","author":"Tsolakis","year":"2023","journal-title":"Ann. Oper. Res."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1080\/10408398.2022.2034735","article-title":"The Fourth Industrial Revolution in the Food Industry\u2014Part I: Industry 4.0 Technologies","volume":"63","author":"Hassoun","year":"2023","journal-title":"Crit. Rev. Food Sci. 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