Bird Species Identification Using Image Processing and CNN

The relationship between birds and humans goes well beyond the the superficial. The process of identifying birds is complex, and mistakes in the naming of species are common. Therefore, photographs based on a computer system are required to aid birdwatchers in the identification process. The proposed work is to explore the feasibility of applying deep learning to the identification of birds by use of a convolutional neural network for feature extraction from photos. In this method, the signature was created by converting a colour picture of a bird to a black-and-white one. Each node's score is calculated using the autographs, and then each species of bird is predicted using the score analysis. Training and evaluation procedures made use of the Caltech-UCSD Birds 200 [CUB-200-2011] dataset. After developing this dataset and employing the similarity comparison algorithm, the effectiveness of the system in practise was demonstrated. Using this system, anyone may quickly learn the name of any bird they're interested in.