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The individual identification of sea turtles through manual and semi-automated methods, such as those used in current photo-identification algorithms like WildID, Hotspotter, and I3S-Pattern, requires images of acceptable quality. This process involves analyzing the geometric morphology of the facial and dorsal scales, where users must manually select the region of interest. However, this methodology faces challenges due to the inherent natural variability of these species and the morphological changes that occur over time. In image processing, automatic semantic segmentation techniques accurately delineate specific areas within images. This enables the detection, identification, and differentiation of objects through pixel-level analysis and visual pattern recognition, highlighting contours and specific object characteristics. In this work, we propose an artificial intelligence model and deep learning approach designed for the individual identification of sea turtles using multiple convolutional neural networks. By integrating the Segment Anything Model (SAM) for image content segmentation into specialized scenarios such as sea turtle identification, this includes automating the semantic segmentation of regions of interest in sea turtle images, as well as edge detection and multimodal text comprehension using Contrastive Language-Image Pre-training models. The individual identification is based on the geometric morphology of the facial scales, shell, as well as the fins and neck of sea turtles. This approach allows for precise and detailed identification by leveraging the unique anatomical features of each specimen. The results obtained demonstrate high performance in terms of precision, supported by the evaluation of metrics such as precision and sensitivity. These metrics provide a quantitative measure of the model's ability to accurately identify individuals.
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