Sistema de reconocimiento y rescate de animales en peligro de extinción mediante aprendizaje profundo

Alejandro Corro Sabino

doi:10.22490/25394088.10444

Vol. 20 No. 1 (2026)

Published 2026-02-23

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Original article

System for recognizing and rescuing endangered animals using deep learning

DOI: https://doi.org/10.22490/25394088.10444

Alejandro Corro Sabino Simón Bolívar University

Abstract
References

WildEye offers a web platform that integrates a convolutional neural network (CNN)-based image classifier with a report management workflow to facilitate the identification and rescue of potentially endangered animals. The system allows users to upload images, obtains the image's geographic location from metadata, classifies the specimen, and assigns the report to the relevant authorities/organizations in the corresponding department and country. The model was implemented using TensorFlow/Keras and trained on a set of over 5,000 images distributed across 90 categories, achieving a reported accuracy of approximately 99% in training and approximately 90% in testing after 35 seasons (loss < 1%).

The article presents the data collection and preprocessing methodology, the training procedure, and the evaluation metrics. It discusses limitations (e.g., the lack of explicit labeling of endangered species in the dataset) and proposes future lines of work (expanding the dataset with conservation status labels, field validation, and adopting detection/segmentation models to strengthen identification). The results demonstrate the potential of integrating deep learning and collaborative platforms to accelerate response to sightings and support conservation actions.

keywords: Deep Learning, Convolutional neural networks, Species recognition, Wildlife rescue, Collaborative platform

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How to Cite

Corro Sabino, A. (2026). System for recognizing and rescuing endangered animals using deep learning. Publicaciones E Investigación, 20(1). https://doi.org/10.22490/25394088.10444

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