# Deep learning for smart fish farming: applications, opportunities and challenges

arXiv: https://arxiv.org/abs/2004.11848 · HTML: https://mlsanity.com/paper/2004.11848/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey](https://mlsanity.com/paper/2203.06951/) (2022)
- [Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook](https://mlsanity.com/paper/2109.14737/) (2021)
- [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://mlsanity.com/paper/2206.05394/) (2022)
- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)
- [Evaluating Deep Learning Assisted Automated Aquaculture Net Pens Inspection Using ROV](https://mlsanity.com/paper/2308.13826/) (2023)
- [AQUA: A Large Language Model for Aquaculture & Fisheries](https://mlsanity.com/paper/2507.20520/) (2025)
- [Internet of Things (IoT) based ECG System for Rural Health Care](https://mlsanity.com/paper/2208.02226/) (2022)
- [IoT based Smart Water Quality Prediction for Biofloc Aquaculture](https://mlsanity.com/paper/2208.08866/) (2022)
- [Autonomous Underwater Robotic System for Aquaculture Applications](https://mlsanity.com/paper/2308.14762/) (2023)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)
- [THPL: A Vision-to-Language Decision Support Framework for Rainbow Trout Feeding Management in RAS](https://mlsanity.com/paper/2610.02378/) (2026)

## Abstract (arXiv)

With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture. This change can create new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on the applications of DL in aquaculture, including live fish identification, species classification, behavioral analysis, feeding decision-making, size or biomass estimation, water quality prediction. In addition, the technical details of DL methods applied to smart fish farming are also analyzed, including data, algorithms, computing power, and performance. The results of this review show that the most significant contribution of DL is the ability to automatically extract features. However, challenges still exist; DL is still in an era of weak artificial intelligence. A large number of labeled data are needed for training, which has become a bottleneck restricting further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs in the handling of complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for the implementation of smart fish farming.