• Sujay GS
• Deepa SR
• R Pratiksha
• Sourabh Santosh Kamble
• Sudhanshu Joshi

Birds are regarded as the most valuable aspect of God's creation. Birds contribute to Earth's biodiversity in various ways. They indicate environmental hazards as they are very much sensitive to habitat and climatic changes. They are considered as a driving force in promoting conservation and also contribute to the economy. Birds aid to control rodent populations and are extremely efficient insect pest controllers. Ornithologists who often work on reporting bird activity need some kind of assistance to deal with the reporting. Numerous bird books have been published to assist birdwatchers and ornithologists in order determining the correct species. However, the identification of birds is an impractical piece of work to be done manually. Image-based bird species identification involves various techniques like Open CV, CNNs and few more which are the most important image processing techniques to predict the type of bird species. Few of the applications have been built to counterbalance the usage of bird books. Nevertheless, they have few drawbacks of their own. This paper performs a survey on the existing systems, the techniques adopted and the challenges faced.

Bird Identification, Convolutional Neural Networks (CNN), Ornithologists, Deep Learning, Image Recognition.

"Bird Species Identification - A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.e515-e517, March-2022, Available :http://www.jetir.org/papers/JETIR2203466.pdf

"Bird Species Identification - A Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppe515-e517, March-2022, Available at : http://www.jetir.org/papers/JETIR2203466.pdf