Comparative Analysis of Cnn Architectures for Bird Species Classification

Avian species identification constitutes a fundamental component in biodiversity conservation, ecological monitoring, and environmental research initiatives. Conventional manual identification approaches frequently demonstrate limitations including extensive time requirements, susceptibility to human error, and difficulties in processing large image collections. The emergence of artificial intelligence frameworks, particularly Convolutional Neural Networks (CNNs), offers transformative potential for automating avian classification tasks with unprecedented accuracy. This investigation presents a methodical evaluation of three prominent CNN architectural families—Residual Neural Network, Visual Geometry Group, and MobileNet_v2—specifically for bird species recognition applications. Utilizing an extensive dataset comprising 84,635 high-resolution images representing 525 distinct avian species, the research implements sophisticated data augmentation strategies to enhance model robustness. Each architectural variant undergoes identical training protocols across 50 epochs with transfer learning from ImageNet pre-trained weights. Experimental results demonstrate that deeper ResNet configurations (ResNet101 and ResNet152) achieve superior taxonomic discrimination with classification accuracy exceeding 95.8%, while MobileNet_v2 establishes an optimal balance between accuracy (94.55%) and computational efficiency. Performance variations are critically analyzed with respect to architectural characteristics including residual connections, network depth, and parameter efficiency. This comprehensive architectural assessment provides valuable frameworks for selecting appropriate deep learning models across diverse ornithological applications, from high-performance research environments to resource-constrained field deployments.