Recognition of Endemic Bird Species Using Deep Learning Models

Numerous bird species have become extinct because of anthropogenic activities and climate change. The destruction of habitats at a rapid pace is a significant threat to biodiversity worldwide. Thus, monitoring the distribution of species and identifying the elements that make up the biodiversity of a region are essential for designing conservation stratagems. However, identifying bird species from images is a complicated and tedious task owing to interclass similarities and fine-grained features. To overcome this, in this study, we developed a transfer learning-based method using Inception-ResNet-v2 to detect and classify bird species endemic to Taiwan and to distinguish them from other object domains. Furthermore, to validate the reliability of the model, we adopted a technique that involves swapping misclassified data between training and validation datasets. The swapped data are retrained until the most suitable result is obtained. Additionally, fivefold cross-validation was performed to verify the predictive performance of the model. The proposed model was tested using 760 images of birds belonging to 29 species that are endemic to Taiwan; the images were captured from various environments with different perspectives and occlusions. Our model achieved an accuracy of 98.39% in the classification of the bird species and 100% in the detection of birds among different object categories. Moreover, the model achieved a precision, recall, and F1-score of 98.49%, 97.50%, and 97.90%, respectively, in classifying bird species endemic to Taiwan.

Automated summary

A transfer learning-based method using Inception-ResNet-v2 was developed to detect and classify bird species endemic to Taiwan, achieving high accuracy in bird species classification and detection among different object categories. The model demonstrated precision, recall, and F1-score exceeding 98% in classifying bird species endemic to Taiwan.