The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing 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 implementing smart fish farming applications.

中文翻译：

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用于智能鱼类养殖的深度学习：应用，机遇和挑战

深度学习（DL）技术的迅速兴起已导致其在包括水产养殖在内的各个领域的成功使用。DL在智能鱼类养殖中为信息和数据处理创造了新的机遇和一系列的挑战。本文着重介绍DL在水产养殖中的应用，包括活鱼识别，物种分类，行为分析，喂养决策，大小或生物量估计以及水质预测。还分析了应用于智能鱼类养殖的DL方法的技术细节，包括数据，算法和性能。审查结果表明，DL的最大贡献在于其自动提取特征的能力。但是，挑战仍然存在。DL仍处于弱势的人工智能阶段，需要大量标记数据进行训练，这已经成为限制水产养殖中进一步DL应用的瓶颈。尽管如此，DL在处理水产养殖中的复杂数据方面仍然提供了突破。简而言之，我们的目的是使研究人员和从业人员更好地了解水产养殖中DL的当前技术水平，这可以为实施智能鱼类养殖应用提供有力的支持。