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    Segmentation-enhanced ensemble deep learning for animal species classification
    (İdris Karagöz, 2026) Ceylan, Mustafa; Fındıkçı, Andaç; Erten, Mustafa Yasin; Aydilek, Hüseyin
    In recent years, there has been a significant surge in the applications of artificial intelligence, with remarkable advancements recorded in their predictive and problem-solving capabilities. Image processing technologies have emerged as a pivotal component of this advancement, enabling the development of models capable of detecting fine details with high accuracy and thereby providing critical solutions for areas such as biodiversity conservation. The accurate identification and classification of animal species are of paramount importance for monitoring endangered species, assessing ecosystem health, and planning conservation efforts. However, traditional methods are often time-consuming, costly, and prone to human error, thus heightening the need for more reliable and efficient systems. In this study, a dataset comprising 74 different animal species was utilized. The images in this dataset were first segmented using the SAM 2 model, after which independent models were trained with deep learning architectures such as InceptionV3, Xception, and DenseNet169, and hyperparameter optimization was performed using the Bayesian search method. To further enhance classification performance, a Soft Voting ensemble learning approach was employed, achieving an accuracy rate of approximately 93%. This proposed model has been named The Deep Pet Ensemble. The results indicate that while artificial intelligence and image processing techniques can serve as powerful tools for animal species recognition, they also present a rapid, cost-effective alternative for supporting biodiversity conservation, ecosystem sustainability, and scientific research.