Abstract: Identifying bird species from images presents a considerable challenge due to the subtle differences between species and the significant variability within species. Although bird species may share similar anatomical characteristics, they can vary greatly in terms of color, shape, and posture. Additional factors such as differing lighting conditions, intricate backgrounds, and various poses—like birds in flight, swimming, or partially hidden while perched—complicate the classification process. Even seasoned ornithologists may encounter ambiguity in species identification based solely on visual information. This study introduces a machine learning-based method designed to assist novice bird watchers in accurately identifying bird species from their photographs. By utilizing image classification models that have been trained on labelled bird datasets, the proposed system is capable of recognizing unique visual patterns and features, thereby offering a scalable and intelligent solution for ornithological identification. This method not only enhances public interest in biodiversity but also aids citizen science and conservation initiatives through the use of artificial intelligence.

Keywords: Birds Identification

| DOI: 10.17148/IARJSET.2025.12579