Identification of Bird Species Using Deep Learning

Abstract

Many avian species are getting harder to find, and even when they are discovered, their categorization could be difficult to predict. Birds, when seen from a distance, may appear in a dazzling array of sizes, shapes, colours, and orientations. Compared to the auditory classification, the visual depiction of bird breed variety is far more extensive. The ability to tell birds apart is greatly enhanced by visual aids such as photographs. The Caltech-UCSD Birds 200 dataset serves as the basis for this method's training and validation data. In order to facilitate comparison, we employ deep convolutional neural networks (DCNN) to convert a picture to a grayscale representation and the tensor flow to build a complex autograph consisting of many nodes. As a result of analysing the various access points to the validation data, a ranking table is constructed. Perhaps it can guess the necessary flock by scanning the scoreboard and picking the bird with the greatest rating. By analysing the dataset (CUB-200-2011), we find that the algorithm achieves an accuracy of 89% for identifying bird species. In the research, Linux and the Tensorflow framework were employed.