A Scalable AI Approach to Bird Species Identification for Conservation and Ecological Planning

This project develops an artificial intelligence (AI)-powered system designed to identify bird species for ecological planning and conservation efforts. Leveraging advanced deep learning techniques, the system utilizes pre-trained models such as EfficientNetB0, DenseNet121, and MobileNet, applying transfer learning to improve the efficiency and accuracy of bird species classification. The dataset, sourced from Kaggle, consists of images from 20 different bird species, which are used to train the model for precise identification. The system aims to provide a scalable solution for bird species identification, which can be expanded to other wildlife monitoring applications.

The project employs a Flask-based backend with a user-friendly web interface built using HTML, CSS, and JavaScript. This web application allows users to upload bird images, receive predictions about the species, and access functionalities like user registration, login, and logout. The application’s core objective is to assist researchers, conservationists, and ecologists in monitoring biodiversity efficiently by automating bird species identification. The system can also contribute to scientific research by enabling better tracking of bird populations.

The deep learning models used in this project ensure high accuracy in bird species classification, which can help identify ecological trends and monitor endangered species. With scalability in mind, the system can be expanded to handle more species in the future. It aims to provide an effective tool for research, conservation planning, and ecological data collection.

Keywords: AI, bird species identification, deep learning, transfer learning, Flask, ecological planning, biodiversity, EfficientNetB0, DenseNet121, MobileNet.