Knowledge on the age of fish is vital for assessing the status of fish stocks and proposing management actions to ensure their sustainability. Prevalent methods of fish ageing are based on the readings of otolith images by experts, a process that is often time-consuming and costly. This suggests the need for automatic and cost-effective approaches. Herein, we investigate the feasibility of using deep learning to provide an automatic estimation of fish age from otolith images through a convolutional neural network designed for image analysis. On top of this network, we propose an enhanced - with multitask learning - network to better estimate fish age by introducing as an auxiliary training task the prediction of fish length from otolith images. The proposed approach is applied on a collection of 5027 otolith images of red mullet (Mullus barbatus), considering fish age estimation as a multi-class classification task with six age groups (Age-0, Age-1, Age-2, Age-3, Age-4, Age-5+). Results showed that the network without multitask learning predicted fish age correctly by 64.4 %, attaining high performance for younger age groups (Age-0 and Age-1, F1 score > 0.8) and moderate performance for older age groups (Age-2 to Age-5+, F1 score: 0.50−0.54). The network with multitask learning increased correctness in age prediction reaching 69.2 % and proved efficient to leverage its predictive performance for older age groups (Age-2 to Age-5+, F1 score: 0.57−0.64). Our findings suggest that deep learning has the potential to support the automation of fish age reading, though further research is required to build an operational tool useful in routine fish aging protocols for age reading experts.

关于鱼龄的知识对于评估鱼类种群状况和提出管理行动以确保其可持续性至关重要。鱼类衰老的流行方法是基于专家对耳石图像的阅读，这一过程通常既耗时又昂贵。这表明需要自动化且具有成本效益的方法。在此，我们研究了使用深度学习通过设计用于图像分析的卷积神经网络从耳石图像自动估计鱼龄的可行性。在这个网络之上，我们提出了一个增强的 - 多任务学习 - 网络，通过引入作为辅助训练任务的耳石图像预测鱼的长度来更好地估计鱼的年龄。所提出的方法应用于 5027 张红鲻鱼耳石图像的集合（鲢)，将鱼龄估计视为具有六个年龄组（0 岁、1 岁、2 岁、3 岁、4 岁、5+ 岁）的多类分类任务。结果表明，没有多任务学习的网络正确预测了 64.4% 的鱼年龄，在较年轻的年龄组（0 岁和 1 岁，F1 分数 > 0.8）上获得了高性能，在年龄较大的年龄组（2 岁到 2 岁）上获得了中等性能-5+，F1 得分：0.50−0.54）。具有多任务学习的网络提高了年龄预测的正确率，达到了 69.2%，并证明可以有效地利用其对老年组（2 岁至 5 岁以上，F1 分数：0.57-0.64）的预测性能。我们的研究结果表明，深度学习具有支持鱼龄阅读自动化的潜力，尽管需要进一步研究以建立一个对年龄阅读专家在常规鱼龄协议中有用的操作工具。