Author(s) :

Akshada balasaheb sonawane 1 , Nayana Vilas deure 2 , Nisha Sitaram Bagade3, Priyanka Sunil jadhav4, Dr.B.S . Shirole5.

Volume/Issue :

Abstract :

Life's routine tempo appears to be rapid and energetic and includes diverse tasks. Bird-watching is a popular hobby which offers relaxation in everyday life. Innumerable people visit bird sanctuaries to observe the elegance of different species of birds. To provide birdwatchers with a convenient tool for identifying the birds in their natural habitat, we developed a Deep Learning model to help birders recognize 60 bird species. We implemented this model to extract information from bird images using the Convolutional Neural Network (CNN) algorithm. We gathered a dataset of our own using Microsoft’s Bing Image Search API v7. We created an 80:20 random split of the data. The classification accuracy rateof CNN on the training set was observed to be 93.19%. The accuracy on testing set was observed to be 84.91%. The entire experimental research was carried out on Windows 1

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