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First Review

The project focuses on developing a machine learning-based system for automated bird species identification using images, audio recordings, and video footage. By leveraging techniques such as Convolutional Neural Networks (CNNs), the system aims to enhance accuracy, reduce identification time, and support conservation efforts. This multimodal approach addresses the challenges faced by traditional identification methods, making it more accessible for researchers and enthusiasts alike.

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swetha saravanan
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16 views13 pages

First Review

The project focuses on developing a machine learning-based system for automated bird species identification using images, audio recordings, and video footage. By leveraging techniques such as Convolutional Neural Networks (CNNs), the system aims to enhance accuracy, reduce identification time, and support conservation efforts. This multimodal approach addresses the challenges faced by traditional identification methods, making it more accessible for researchers and enthusiasts alike.

Uploaded by

swetha saravanan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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MINOR PROJECT III

FIRST REVIEW

TEAM MEMBERS GUIDED BY:


JANANI P (927622BAD020) MR.K.JEYA GANESH KUMAR M.E.,(Ph.D.)
JOVITHA R (927622BAD023)
KANIMOZHI P (927622BAD025)
TITLE :PREDICTING BIRD
SPECIES
INTRODUCTION
• Significance: Bird species identification is essential for
ecological studies, conservation, and biodiversity
monitoring.
• Challenges: Traditional identification methods are time-
consuming and require expert knowledge, making it
difficult to scale.
• Machine Learning Advantage: Advances in machine
learning enable automated and scalable bird
identification, enhancing accuracy and efficiency.
• Data Sources: Digital images, sound recordings, and
environmental data are key inputs for machine learning
models in bird species prediction.
• Techniques: Techniques like Convolutional Neural
Networks (CNNs) are popular for image classification
tasks, helping distinguish between bird species based on
patterns in images or sounds.
PROBLEM STATEMENT
• Bird species identification is crucial for biodiversity
monitoring, ecological research, and conservation efforts.
However, manually classifying birds based on images, audio
recordings, or videos is labor-intensive, time-consuming, and
prone to human error. With the growth of data from various
sources an automated system capable of efficiently and
accurately identifying bird species is essential.
• To address this challenge, we propose a multimodal system
that leverages images, audio recordings of bird songs and
calls, as well as video footage to predict bird species. The
system will combine state-of-the-art techniques in computer
vision, audio analysis, and machine learning to create a
robust and scalable solution.
OBJECTIVES

Develop a machine learning model capable of classifying bird species from


images with high accuracy.

Reduce the time required for species identification, making it accessible to


both researchers and enthusiasts.

Create a scalable solution that can be used for large-scale monitoring and
data collection on bird populations.

Provide an automated tool that can support conservation efforts, educational


initiatives, and citizen science projects.
ABSTRACT
• Identifying bird species from images, videos, and audio recordings presents
significant challenges due to ambiguous labels, high intraclass variance, and the
dramatic variations in bird shapes, appearances, and poses. Lighting conditions
and diverse backgrounds further complicate the task. In this project, we leverage
machine learning techniques to assist amateur bird watchers in identifying bird
species through captured media.
• We propose a multimodal system that integrates Convolutional Neural Networks
(CNNs) for image classification and deep learning models for audio recognition.
By utilizing a comprehensive dataset of bird images, videos, and sounds, our
system provides accurate species identification. The user-friendly interface
enhances the experience for birdwatchers, researchers, and conservationists alike,
contributing to ecological monitoring and conservation efforts. By harnessing
machine learning, our system makes bird species identification more accessible
and efficient for all users.
EXISTING SYSTEM
Merlin Bird ID:
•An app developed by Cornell Lab of Ornithology that
identifies bird species using images and audio recordings.
•It requires user input for location and habitat but may
struggle with ambiguous or rare species.
BirdNET:
•An audio-based bird identification tool that analyzes bird
calls using deep learning.
•While effective in quiet environments, it is less accurate in
noisy areas or when multiple birds are vocalizing
simultaneously.
iNaturalist:
•A citizen science platform that uses AI to identify birds
from user-uploaded images.
• It works well for common species but can be imprecise
with poor image quality or challenging bird poses.
FEATURE EXTRACTION PARADIGM FOR BIRD IMAGES
CNN ARCHITECTURE FOR DETECTING BIRD IMAGES
CLIENT-SERVER ARCHITECTURE FOR BIRD DETECTION
Comparison (with Existing)

• Higher Accuracy: Leveraging CNNs for pattern recognition in images,


the model achieves higher accuracy than traditional field guides or
mobile apps.
• Faster Identification: Automated identification is significantly faster
than manual methods, providing instant results.
• Improved Usability: The system can be accessed by anyone, reducing
reliance on experts and increasing accessibility for non-specialists.
• Scalability: The model can handle large datasets and identify
thousands of species, making it suitable for large-scale ecological
studies and citizen science.
Conclusion
• This project aims to create a robust and scalable bird species
identification system that leverages deep learning to overcome the
limitations of traditional methods. By automating the identification
process, this system has the potential to benefit ecological research,
conservation efforts, and citizen science initiatives. It can facilitate
large-scale biodiversity monitoring, providing critical data to
researchers and conservationists. Future improvements could include
expanding the dataset, enhancing model accuracy, and exploring
applications in real-time monitoring using camera traps or mobile
apps. This project represents a step toward more accessible and
efficient bird species identification, contributing to broader ecological
and conservation goals.

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