IEEE BASE PAPER TITLE:
Image-Based Bird Species Identification Using Machine Learning
OUR PROPOSED PROJECT TITLE:
An Intelligent Image-Based Bird Species Identification System using Deep Learning
IEEE BASE PAPER ABSTRACT:
Along with humans, birds lead lovely lives and are amazing creatures. One of the signs of climatic change is birds. From mid-level consumers to top predators, birds play a significant role in all trophic levels. Currently, extinction threatens many of these bird species. Naturally, each bird is unique in terms of its traits as well as its physical attributes, including shape, size, beak, feathers, silhouette, and others. As opposed to audio-based classification, bird photos are particularly useful in identifying species. By far, the most comfortable method for humans to identify birds is by visual classification. One of the crucial components of image categorization is the dataset of birds, which has been gathered. From the input image, the features are retrieved, and then classification is performed. Both regression and classification are performed using the Random Forest method. To create autographs using tensor flow, the input image is first transformed to grayscale and then to matrix format. As a consequence, the characteristics of the provided bird image are extracted, the bird’s name is determined, and the bird’s origin is also shown.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
OUR PROPOSED PROJECT ABSTRACT:
The identification of bird species plays a vital role in various domains, including wildlife conservation, ecological research, and biodiversity monitoring. However, manual identification of bird species from images can be a time-consuming and error-prone task, especially with the vast number of avian species present worldwide. The project “Image-Based Bird Species Identification Using Deep Learning” presents a novel and highly accurate approach for automatically classifying bird species from images using the powerful Xception architecture. Developed entirely in Python, this project aims to address the challenging task of recognizing a diverse range of bird species with high precision. The project addresses a critical need in the field of ornithology and computer vision. The core of the system lies in the utilization of the Xception deep learning model, renowned for its exceptional ability to extract intricate features from images, enabling it to capture fine-grained details that are crucial for accurate bird species identification. Through meticulous training and optimization, the model has achieved an impressive training accuracy of 99% and a validation accuracy of 97%, showcasing its efficacy in handling complex classification tasks. The project’s success is further bolstered by the extensive dataset it employs, comprising a comprehensive collection of 60,388 bird images spanning 510 distinct species. This dataset diversity allows the model to learn from a vast array of avian features, ensuring robust performance even when faced with previously unseen species. The proposed image-based bird species identification system finds valuable applications in wildlife monitoring, ecological research, and bird watching enthusiasts, among others. By leveraging the power of deep learning and the Xception architecture, it sets new benchmarks in the realm of bird species recognition, making it a valuable contribution to the field of computer vision and ornithology. This project demonstrates an exceptional solution to the challenge of bird species identification, outperforming conventional methods and opening avenues for further research and application. Its high accuracy, efficient implementation in Python, and use of the Xception architecture position it as a pioneering and unique endeavor in the realm of image-based bird species classification.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 6 GB
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9
- Web Framework : Flask
Persia Abishal B; Sujitha Juliet, “Image-Based Bird Species Identification Using Machine Learning”, 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE Conference, 2023.