Bird species identification using neural networks: Empowering bird watching with technological precision

Abstract

For computer vision and bioacoustics, the identification of bird species is a major problem. We suggest a method for automatic classification of bird species by means of artificial intelligence in this study. We use deep convolutional neural network (CNN) to extract discriminative features from pictures of bird and apply transfer learning to tune the CNN for the specific task of bird species identification. We incorporate data augmentation and hyperparameter optimization techniques to enhance the robustness of the models. We assess the effectiveness of our suggested method using a dataset that is accessible to the public and contrast it with multiple cutting-edge techniques. Our test findings show that our suggested strategy works better than current techniques, classifying 20 bird species with an accuracy of more than 90%. The suggested method can aid in the comprehension and preservation of avian biodiversity and has potential uses in species conservation, habitat assessment, and biodiversity monitoring.