Bird Species Identification from Image

Abstract—Identifying bird species from photos can be surpris- ingly tricky. Even professional bird watchers sometimes disagree on what species they’re looking at. That’s because birds, while sharing common features, can look very different depending on their species, the environment, and how the photo was taken. A single species might appear in a wide range of situa- tions—flying through the sky, swimming on water, or perched on a branch partially hidden by leaves. Lighting conditions and background clutter only add to the challenge. Even small changes in pose or angle can make a big difference in appearance. These challenges make bird identification a tough job for both humans and computers. Our project is focused on using machine learning to help with this. By analyzing bird images and learning from patterns, we aim to create a tool that helps amateur bird watchers identify bird species more accurately and confidently from the photos they take. Key words —Deep learning, convolution neural networks (CNNS),feature extraction, object detection, image segmentation, image classification, image processing.