Deep learning framework for automated otolith growth increment counting in Chloroscombrus chrysurus, a subtropical fish.

Reliable age estimation is essential for understanding fish population dynamics and supporting sustainable fisheries management. Traditionally, fish age estimation relies on manual counting of growth increments in otoliths, a procedure that requires extensive expertise, is time-consuming, and may be affected by reader subjectivity. This dataset supports the development and evaluation of a deep learning framework for automated otolith increment counting and age estimation in the Atlantic bumper (Chloroscombrus chrysurus). The main objective of this study was to evaluate the feasibility of a deep learning approach for automating the counting of otolith growth increments in a subtropical fish species. The dataset comprises 998 sagittal otolith images (TIFF format) collected from individuals sampled along the southeastern Brazilian Bight and provided by the Collection of Calcified Fish Structures – FishCAST (Vaz-dos-Santos, 2026). Each image is associated with biological information in a metadata file (CSV format), including total length and manually validated growth increment counts (Nr). The dataset includes five increment classes (Nr-3, Nr-4, Nr-5, Nr-6, and Nr-7), representing the biological categories used for model training and evaluation. The provided Python scripts implement a convolutional neural network based on the InceptionV3 architecture, adapted through transfer learning and multi-task learning to simultaneously perform increment-class classification and fish length prediction. The computational workflow includes image preprocessing, dataset organization, data augmentation, model training and fine-tuning, prediction generation, and model performance evaluation. Results obtained from this dataset demonstrate the feasibility of using deep learning for automated otolith increment classification in a subtropical fish species with complex increment periodicity. The model correctly classified increment classes with an overall accuracy of 55.5%, showing higher predictive performance for older increment classes (Nr-5 to Nr-7) and moderate performance for younger classes (Nr-3 and Nr-4). These predictions can subsequently be interpreted biologically by converting increment classes into age estimates according to validated species-specific increment formation patterns.精准的鱼类年龄估计，是解析鱼类种群动态、支撑可持续渔业管理的核心前提。传统鱼类年龄估计需人工计数耳石（otolith）的生长轮纹，该流程不仅需要深厚的专业积累，且耗时漫长，同时易受阅片者主观因素干扰。 本数据集可用于开发并评估一套深度学习框架，以实现大西洋圆鲹（Chloroscombrus chrysurus）耳石生长轮纹的自动计数与年龄估计。本研究的核心目标为：评估深度学习方法对亚热带鱼类耳石生长轮纹实施自动化计数的可行性。 该数据集包含998张矢状耳石（sagittal otolith）图像，格式为TIFF，采集自巴西东南近岸海域的捕捞个体，由钙化鱼类结构馆藏库FishCAST（Vaz-dos-Santos, 2026）提供。每张图像均关联CSV格式元数据文件中的生物学信息，包括个体总体长以及经人工验证的生长轮纹计数结果（Nr）。数据集涵盖5类轮纹等级（Nr-3、Nr-4、Nr-5、Nr-6及Nr-7），上述等级为模型训练与评估所采用的生物学分类单元。 配套提供的Python脚本实现了基于InceptionV3架构的卷积神经网络（convolutional neural network），该模型通过迁移学习与多任务学习进行适配优化，可同时完成轮纹等级分类与鱼类体长预测。整套计算流程涵盖图像预处理、数据集组织、数据增强、模型训练与微调、预测结果生成以及模型性能评估环节。 基于本数据集得到的实验结果证实，针对轮纹形成周期复杂的亚热带鱼类，采用深度学习实现耳石轮纹自动化分类具备可行性。模型对轮纹等级的总体分类准确率达55.5%，对较老龄轮纹等级（Nr-5至Nr-7）的预测性能更佳，而对较幼龄等级（Nr-3与Nr-4）的预测性能处于中等水平。后续可依据经过验证的物种特异性轮纹形成规律，将轮纹等级转换为年龄估计值，从而完成生物学层面的解读。