Bird species detection using deep learning image analysis

Birds play a crucial role in ecosystem balance, contributing to multiple trophic levels and serving as bioindicators of climate change. Accurate species identification is essential for conservation and ecological studies. Traditional classification methods often struggle with accuracy and scalability, making image-based deep learning models a more effective alternative. This study employs a Deep Convolutional Neural Network (DCNN) approach using the Caltech-UCSD Birds 200 dataset for training and validation. Models such as VGG16, InceptionV3, and ResNet are implemented with TensorFlow to construct a computational graph for feature extraction and species classification. Comparative analysis demonstrates that InceptionV3 achieves the highest accuracy (93.91%), outperforming VGG16 (82.28%) and other models. The proposed deep learning approach significantly surpasses traditional methods, establishing its effectiveness for automated bird species recognition.