Identification of Indian Bird Species to Promote Conservation Endeavors

Abstract

Birds are vertebrate animals that are found all across the globe and are an integral part of most ecosystems. With a large variety of birds found globally and the significant role they play in conservation planning and decision-making, there is an increased interest shown by researchers, students as well as hobbyists to study them. Due to the many similarities among subspecies of birds, it becomes difficult to identify them and thus, a system that can help in the identification process is much desired. Using image classification techniques, bird identification systems can be developed which can be used to identify bird species by using their images. The current development in this domain is quite promising with many systems being developed for identifying birds using either their images, videos, or sounds, but most of them have been developed for North American birds. The purpose of our project is to develop a bird identification system specifically for Indian birds to provide a convenient identification tool to the students, researchers, and hobbyists in India. We make use of the GoogLeNet pre-trained classification model and apply transfer learning techniques to train the model on our dataset of Indian birds. The model yielded an accuracy of 99.51% on the testing set, one of the highest among the existing identification systems for Indian birds.