Deep Learning for Automated Animal Species Classification: A Review of Single-Model and Hybrid Approaches Across Images and Sound

Identifying animals manually in camera-trap images, underwater images and sound recordings is slow and costly, but recording species is the first step in protecting animals. Deep learning can identify species automatically, but studies use different data and different scores, so it is hard to know which approach works best. This review examines ten deep-learning studies from 2023 to 2025 and one field dataset. It compares single-model approaches, such as convolutional neural networks and transformers, with hybrid approaches that join two or more models or kinds of data. To keep the comparison fair, hybrid and single models are compared only within the same study, on the same data and with the same measure, such as accuracy or F1-score. Hybrids scored higher than single models in every study that tested both, but in four of the five studies the gain was only 0.41 to 4.2 points, and only one study reported the extra cost. The type of data mattered more than the type of model. The same models fell from a mean average precision of 0.71 on clean recordings to 0.311 on field soundscapes, and the highest accuracies came from studies whose data splits may let near-copies of examples appear in both training and test sets. The remaining problems are missing spread between runs, missing costs, data leakage, weak results on rare species and the drop on field data. Future studies should report repeated runs and costs, split data by recording or site, and test on shared field datasets