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  2. Vol 10, No 2 (2026)
  3. AMONGA

A Hybrid ResNet–Inception Deep Learning Architecture for Enhanced Wildlife Identification

MALACH OBISA AMONGA(1),


(1) CHUKA UNIVERSITY
Corresponding Author

Abstract


Accurate wildlife identification in unconstrained environments remains a significant challenge due to intra-class similarity, occlusions, illumination variations, and complex backgrounds. This study of hybrid deep learning architecture that enhances classification performance by integrating complementary feature learning mechanisms within a unified framework. The model captures both deep hierarchical representations and multi-scale spatial features through a feature-level fusion strategy, resulting in a more discriminative and robust representation of wildlife images. The hybrid approach was trained and evaluated on the Animals with Attributes 2 (AwA2) dataset and further validated on the WildlifeReID-10k dataset to assess its generalization capability. Experimental results show that the model achieves a classification accuracy of 98%, along with high precision, recall, and F1-score, demonstrating strong reliability and effectiveness. Furthermore, the model maintains high performance on external data, confirming its robustness and ability to generalize across diverse real-world conditions. These findings highlight the effectiveness of hybrid deep learning approaches in addressing complex wildlife identification challenges and support their application in ecological monitoring and biodiversity conservation

Keywords


Hybrid Deep Learning, Wildlife Identification, Feature Fusion, Computer Vision, Image Classification, Ecological Monitoring.

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DOI: 10.56327/ijiscs.v10i2.1889

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