Non-invasive fish identification of individuals can provide new possibilities for the monitoring of fish cultivation, improve and make fish production technologies less demanding for farmers, and increase fish welfare. The aim of this research is to confirm the idea of automatic non-invasive image-based fish identification of individuals using visible features on a fish body and prove the pattern stability during the fish cultivation period. Visible patterns, such as black stripes along the body of a Sumatra barb (Puntigrus tetrazona), were used for machine identification of individual fish. Two experiments were completed: a short-term experiment (43 fish) to show the uniqueness of the stripe patterns for identification, and a long-term experiment (25 fish) to test the stability of patterns during the cultivation period. The overall accuracy of classification was 100% for data collection in one day and 88% between two data collection times. This study shows that visible patterns and image processing methods can be used to automatically identify individual fish of the same species. This is not just limited to Sumatra barb—the concept should work for any fish with unique visible skin patterns, for example, for commercial fish species like Atlantic salmon (Salmo salar) and European perch (Perca fluviatilis).

个体的非侵入式鱼类识别可以为监测鱼类养殖提供新的可能性，改善并降低鱼类生产技术对农民的要求，并增加鱼类福利。这项研究的目的是要确认利用鱼体上的可见特征对个体进行基于非侵入式图像的自动鱼识别的想法，并证明鱼在养殖期间的模式稳定性。可见的图案，例如苏门答腊倒钩（Puntigrus tetrazona）身上的黑色条纹），用于机器识别单个鱼。完成了两个实验：一个短期实验（43条鱼）显示条纹图案的唯一性，而一项长期实验（25条鱼）测试条纹图案在培养期间的稳定性。一天之内数据收集的总体分类准确度为100％，两次数据收集之间的分类准确度为88％。这项研究表明，可见图案和图像处理方法可用于自动识别同一物种的单个鱼。这不仅限于苏门答腊倒钩-该概念适用于任何具有独特可见皮肤图案的鱼，例如，适用于大西洋鲑（Salmo salar）和欧洲鲈（Perca fluviatilis）等商业鱼类。