An Image Mosaicing-Based Method for Bird Identification on Edge Computing Devices.

Bird identification is an important task in wildlife monitoring and conservation. However, traditional methods for bird identification often require significant computational resources, making them impractical for use on edge computing devices. In this paper, we propose an image mosaicing-based method for bird identification on edge computing devices. Our method combines image mosaicing and deep learning techniques to accurately identify bird species from a series of individual images. It achieves excellent accuracy of 99.51% for ResNet50 and 97.37% for MobileNetV2, depending on the specific use case. These results demonstrate the effectiveness and potential of our approach for real-time bird recognition and identification on edge computing devices, making it suitable for a wide range of applications including monitoring and conservation of bird populations.