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AI-driven aquaculture: A review of technological innovations and their sustainable impacts

Artificial Intelligence in Agriculture 2025年第3期15卷 508-525页

作者： Yang, Hang Feng, Qi Xia, Shibin Wu, Zhenbin Zhang, Yi Institute of Hydrobiology Chinese Academy of Sciences Wuhan430072 China Hubei Key Laboratory of Mineral Resources Processing and Environment Wuhan University of Technology Wuhan430070 China University of Chinese Academy of Sciences Beijing100049 China

The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture. © 2024 The Authors

关键词： Aquaculture AI integration Predictive analytics Sustainable aquaculture AI disease management AI feeding optimization AHPND Acute Hepatopancreatic Necrosis Disease AI Artificial Intelligence AlphaFamImpute Alpha Family Impute AMA-Net Appearance-Motion Architecture Network ANN Artificial Neural Network ASST Acoustic Spectrogram Signal Transformer AUC Area Under the Curve BFT Biofloc Technology BP Back Propagation BPNN Back Propagation Neural Network CBAM Convolutional Block Attention Module CNN Convolutional Neural Network CVS Computer Vision System DM-Count Density Map Count DCNN Deep Convolutional Neural Network DL Deep learning DO Dissolved Oxygen EEMD Ensemble Empirical Mode Decomposition EER Equal Error Rate FLOPs Floating Point Operations Per Second FPS Frames Per Second FFINet Feature Fusion Interactive Network FT-IR Fourier Transform Infrared Spectroscopy GA Genetic Algorithm GBLUP Genomic Best Linear Unbiased Prediction GELU Gaussian Error Linear Unit GIS Geographic Information System GRU Gated Recurrent Unit GWAS Genome-Wide Association Studies HPO Hyperparameter Optimization HSI Hyperspectral Imaging IoT Internet of Things KHV Koi Herpes Virus LCA Linear Calibration Algorithm LCNN Local Convolutional Neural Network LDA Linear Discriminant Analysis LGBM Light Gradient Boosting machine LIA Linear Integration Algorithm LSTM Long Short-Term Memory MAE Mean Absolute Error mAP Mean Average Precision MCC Matthews Correlation Coefficient ML machine learning ML-KAML machine learning - kernel additive model learning MLP Multilayer Perceptron MSE Mean Squared Error NB Naïve Bayes NIR Near-Infrared NMR Nuclear Magnetic Resonance NSE Nash-Sutcliffe Efficiency PBLUP Pedigree-Based Best Linear Unbiased Prediction PCA Principal Component Analysis PCC Pearson Correlation Coefficient PNN Probabilistic Neural Network PSO Particle Swarm Optimization QSAR Quantitative Structure-Activity Relationship QUEST Quick Unbiased Efficient Statistical Tree RAS Recirculating Aquaculture System RF Random Forest RFerns Random Ferns RMSE

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