• Dr. K.K.Tripathi
• Uday Gowda
• Rahul Gupta
• Subhojit Jana
• Pankaj Chavan

This project focuses on the development of a system for animal and bird species identification using Convolutional Neural Networks (CNNs). Wildlife conservation and ecological research demand accurate species recognition, and the proposed system employs deep learning to fulfill this need. By training the CNN on a diverse dataset of animal and bird images, the system achieves high accuracy in species identification, contributing to conservation efforts. The project addresses the urgency of species preservation in the face of environmental threats. Traditional methods of species identification are often time- consuming and prone to human error. The CNN-based approach offers a more efficient and accurate solution. The foundation of the project lies in a comprehensive image dataset, encompassing a wide range of species, enabling the CNN to recognize subtle visual distinctions. This project not only involves training the CNN but also creating a user-friendly interface for easy image upload and rapid species identification. This accessibility ensures that a broad audience can benefit from this technology, supporting the cause of species preservation. By leveraging CNNs, this project represents a powerful tool for wildlife conservation, accelerating the process of species identification, facilitating effective conservation measures, and aiding in the monitoring of biodiversity. It highlights the importance of collaborative efforts to protect our planet's natural heritage.

Convolutional Neural Networks(CNN), Species Identification.

"Species Identification from An Image", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.199-206, July-2024, Available :http://www.jetir.org/papers/JETIRGK06026.pdf

"Species Identification from An Image", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp199-206, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06026.pdf