Lonati Martina, Jahanbakht Mohammad, Atkins Danielle, Bierwagen Stacy L, Chin Andrew, Barnett Adam, Rummer Jodie L
College of Science and Engineering, James Cook University, Douglas, Australia.
Marine Data Technology Hub, James Cook University, Townsville, Australia.
J Fish Biol. 2024 Dec;105(6):1572-1587. doi: 10.1111/jfb.15887. Epub 2024 Aug 10.
Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs well with some species of elasmobranchs (i.e., sharks, skates, and rays) where individuals have distinctive skin patterns. However, the unique skin patterns used for ID must be stable through time to allow re-identification of individuals in future sampling events. More recently, artificial intelligence (AI) models have substantially decreased the labor-intensive process of matching photos in extensive photo ID libraries and increased the reliability of photo ID. Here, photo ID and AI are used for the first time to identify epaulette sharks (Hemiscyllium ocellatum) at different life stages for approximately 2 years. An AI model was developed to assess and compare the reliability of human-classified ID patterns in juvenile and neonate sharks. The model also tested the persistence of unique patterns in adult sharks. Results indicate that immature life stages are unreliable for pattern identification, using both human and AI approaches, due to the plasticity of these subadult growth forms. Mature sharks maintain their patterns through time and can be identified by AI models with approximately 86% accuracy. The approach outlined in this study has the potential of validating the stability of ID patterns through time; however, testing on wild populations and long-term datasets is needed. This study's novel deep neural network development strategy offers a streamlined and accessible framework for generating a reliable model from a small data set, without requiring high-performance computing. Since many photo ID studies commence with limited datasets and resources, this AI model presents practical solutions to such constraints. Overall, this approach has the potential to address challenges associated with long-term photo ID data sets and the application of AI for shark identification.
照片识别是一种既定的方法，用于统计动物数量和追踪个体的活动。这种方法在一些具有独特皮肤图案的板鳃亚纲动物（即鲨鱼、鳐鱼和魟鱼）中表现良好。然而，用于识别的独特皮肤图案必须随时间保持稳定，以便在未来的采样活动中重新识别个体。最近，人工智能（AI）模型大幅减少了在大量照片识别库中匹配照片这一劳动密集型过程，并提高了照片识别的可靠性。在这里，照片识别和人工智能首次被用于识别肩章鲨（Hemiscyllium ocellatum）在不同生命阶段的情况，为期约两年。开发了一个人工智能模型来评估和比较幼年和新生鲨鱼中人工分类的识别图案的可靠性。该模型还测试了成年鲨鱼独特图案的持久性。结果表明，由于这些亚成年生长形态的可塑性，无论是使用人工还是人工智能方法，未成熟的生命阶段在图案识别方面都不可靠。成熟的鲨鱼随时间保持其图案，并且可以被人工智能模型以大约86%的准确率识别。本研究中概述的方法有可能验证识别图案随时间的稳定性；然而，需要在野生种群和长期数据集上进行测试。本研究新颖的深度神经网络开发策略提供了一个简化且易于使用的框架，可从小数据集中生成可靠的模型，而无需高性能计算。由于许多照片识别研究始于有限的数据集和资源，这种人工智能模型为这些限制提供了切实可行的解决方案。总体而言，这种方法有可能应对与长期照片识别数据集以及人工智能在鲨鱼识别中的应用相关的挑战。
