Bird Species Identification System

Authors

  • S. Bhaggiaraj Dept. of Information Technology, Sri Ramakrishna Engineering College, Affiliated by Anna University, Coimbatore, India
  • K. Boomathi Dept. of Information Technology, Sri Ramakrishna Engineering College, Affiliated by Anna University, Coimbatore, India
  • V Kavya Dept. of Information Technology, Sri Ramakrishna Engineering College, Affiliated by Anna University, Coimbatore, India
  • S. Mathura Dept. of Information Technology, Sri Ramakrishna Engineering College, Affiliated by Anna University, Coimbatore, India

DOI:

https://doi.org/10.26438/ijcse/v8i5.128131

Keywords:

Machine Learning, Random Forest

Abstract

Birds are amazing creatures, and having wonderful lives right along with humans. Birds are one of the indicators of climatic changes. They help in maintaining the environment and food chain by eating pest and this result in ecological balance. Naturally, each and every bird differs from one another by their characteristics and also with the body features like shape, size, color, beak, feathers and silhouette etc. The images of birds are very much helpful in finding the species rather than audio based classification. Humans are more comfortable to recognize the birds through image classification than any other ways of classification. The dataset of birds are collected and it is one of the important parts in image classification. Image classification is the process of taking an input and outputting the probability that the input is a particular class .By using Machine learning technique called Random Forest, the input image is converted into grey scale format to generate autograph by using tensor flow. As a result of this, the features of the given bird image is extracted and name of the bird is identified along with its origin.

References

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Published

2020-05-31
CITATION
DOI: 10.26438/ijcse/v8i5.128131
Published: 2020-05-31

How to Cite

[1]
S. Bhaggiaraj, K. Boomathi, V. Kavya, and S. Mathura, “Bird Species Identification System”, Int. J. Comp. Sci. Eng., vol. 8, no. 5, pp. 128–131, May 2020.

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Section

Research Article