Low Cost Journal,International Peer Reviewed and Refereed Journals,Fast Paper Publication approved journal IJEDR(ISSN 2321-9939) apply for ugc care approved journal, UGC Approved Journal, ugc approved journal, ugc approved list of journal, ugc care journal, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, Low cost research journal, Online international research journal, Peer-reviewed, and Refereed Journals, scholarly journals, impact factor 7.37 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool)
INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH
(International Peer Reviewed,Refereed, Indexed, Citation Open Access Journal)
ISSN: 2321-9939 | ESTD Year: 2013

Current Issue

Call For Papers
July 2022

Volume 10 | Issue 3
Last Date : 29 July 2022
Review Results: Within 12-20 Days

For Authors

Archives

Indexing Partner

Research Area

LICENSE

Paper Details
Paper Title
Bird Species Classification using multi-scale Convoluted Neural Network with Data Augmentation Techniques
Authors
  Pankaj Prakash Patil,  Atharva Dhananjay Kulkarni,  Aakash Ajay Dhembare,  Krishna Adar,  Rahul Sonkamble

Abstract
Bird predation is a major problem in aquaculture. Nowadays bird Species are becoming rare, so we need to recognize them. Image recognition software can improve their efficiency in chasing birds. We proposed the System for Bird species Classification is a challenging problem due to the variation and different viewpoints of the camera. In the existing system, there are some disadvantages. We tried to overcome it by integrating the new feature into the multi-scale Convoluted Neural Network with Image Segmentation for Indian bird species classification, an algorithm is proposed to get the final classification result. Three recognition techniques were tested to identify birds i.e., image morphology, artificial neural networks, and template matching have been tested. We proposed a new feature that can improve the correct classification rate of the model as well as the accuracy of the model in the prediction of Birds classification. In this challenge, the bird image classification task, especially for Indian birds, is based on a limited but diverse set of crowd-sourced data. Especially, the present challenge involves a low amount of labelled data to build good classification approaches for effective classification. Up to now a lot of research has been done to identify bird species. Finally, we have proposed a methodology to improve accuracy in the identification of bird species.

Keywords- Data Augmentation, Dropout, TensorFlow, Keras, DT, CNN, Multiscale
Publication Details
Unique Identification Number - IJEDR2102047
Page Number(s) - 289-291
Pubished in - Volume 9 | Issue 2 | June 2021
DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.27387
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Pankaj Prakash Patil,  Atharva Dhananjay Kulkarni,  Aakash Ajay Dhembare,  Krishna Adar,  Rahul Sonkamble,   "Bird Species Classification using multi-scale Convoluted Neural Network with Data Augmentation Techniques", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.9, Issue 2, pp.289-291, June 2021, Available at :http://www.ijedr.org/papers/IJEDR2102047.pdf
Share This Article


Article Preview

ISSN Details




DOI Details



Providing A digital object identifier by DOI
How to get DOI?

For Reviewer /Referral (RMS)

Important Links

NEWS & Conference

Digital Library

Our Social Link

© Copyright 2022 IJEDR.ORG All rights reserved