Deep Learning for Sustainable Aquaculture: Opportunities and Challenges

An-Qi Wu, Ke-Lei Li, Zi-Yu Song, Xiuhua Lou, Pingfan Hu, Weijun Yang () and Rui-Feng Wang ()
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An-Qi Wu: China Agricultural University, Haidian, Beijing 100083, China
Ke-Lei Li: China Agricultural University, Haidian, Beijing 100083, China
Zi-Yu Song: China Agricultural University, Haidian, Beijing 100083, China
Xiuhua Lou: China Agricultural University, Haidian, Beijing 100083, China
Pingfan Hu: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA
Weijun Yang: China Agricultural University, Haidian, Beijing 100083, China
Rui-Feng Wang: China Agricultural University, Haidian, Beijing 100083, China

Sustainability, 2025, vol. 17, issue 11, 1-29

Abstract: With the rising global demand for aquatic products, aquaculture has become a cornerstone of food security and sustainability. This review comprehensively analyzes the application of deep learning in sustainable aquaculture, covering key areas such as fish detection and counting, growth prediction and health monitoring, intelligent feeding systems, water quality forecasting, and behavioral and stress analysis. The study discusses the suitability of deep learning architectures, including CNNs, RNNs, GANs, Transformers, and MobileNet, under complex aquatic environments characterized by poor image quality and severe occlusion. It highlights ongoing challenges related to data scarcity, real-time performance, model generalization, and cross-domain adaptability. Looking forward, the paper outlines future research directions including multimodal data fusion, edge computing, lightweight model design, synthetic data generation, and digital twin-based virtual farming platforms. Deep learning is poised to drive aquaculture toward greater intelligence, efficiency, and sustainability.

Keywords: deep learning; sustainable aquaculture; fish detection and behavior analysis; water quality monitoring; intelligent feeding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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