Bird Species Classification Enhancement via Adaptive Inertia Weight Particle Swarm Optimization-Based Image Augmentation Selection

Automatic bird species identification is challenging due to species diversity, image variability, and dataset limitations that often lead to model overfitting. To address these issues, this study introduces a fusion of augmentation techniques to increase dataset diversity and improve model generalization. Unlike previous approaches with fixed augmentation strategies, this study uses Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) to dynamically select effective combinations of augmentations. In the AIWPSO framework, the inertia weight function guides the optimization process toward an optimal solution. Experiments on the CVIP 2018 Bird Species Challenge dataset show that this adaptive approach significantly improves model performance, boosting training accuracy by 3% and validation accuracy by 25% over prior methods. These results highlight AIWPSO's advantage in helping models generalize effectively across diverse bird species. Overall, this study demonstrates AIWPSO's potential to advance automated bird species identification by optimizing data augmentation strategies and enhancing accuracy in complex classification tasks.