An Enhanced EfficientNet-Powered Wildlife Species Classification for Biodiversity Monitoring

Monitoring wildlife species is crucial for understanding and conserving biodiversity, but efficiently identifying these species across vast natural habitats poses a substantial challenge. The existing deep learning architectures like Inception and AlexNet have drawbacks due to their higher computational demands, which could limit their use in resource-restricted settings. This project leverages the power of EfficientNet, a cutting-edge deep learning architecture, to revolutionize wildlife species classification. The proposed Efficient Model has the limitation of fixed scaling coefficients, resulting in diminishing of accuracy gain. By employing transfer learning methodologies, we fine-tune pre-trained EfficientNet models using expansive datasets comprising diverse wildlife species images. Utilizing advanced preprocessing methods alongside EfficientNet’s capabilities, such as feature normalization and adaptive learning rates, contributes significantly to accuracy gain by refining model robustness. EfficientNet architecture, by employing advanced techniques such as data augmentation and attention mechanisms play a pivotal role, not only in elevating species classification accuracy but also in significantly reducing the computational resources and processing time required. This innovative approach holds immense promise for expediting the cataloging of wildlife, thereby fostering advancements in biodiversity monitoring and benefiting conservation initiatives.