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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

Impact Factor: RJIF 5.2

International Journal of Applied Research

Vol. 6, Issue 5, Part F (2020)

Image classification using machine learning

Author(s)
G Sai Sirisha, I Mounika, K Mukesh and B Dharani
Abstract
Flower Species Recognition is a very hard and challenging mission to identify different types of flowers as they are very similar. The idea of automated flower recognition is bewildering as the flowers are not rigid objects and their images can be affected by many external influences .The existing system is based on classifying flowers into different categories which has very less accuracy. Now the proposed system use machine learning algorithm to fully automate and increase the accuracy of flower classification. Machine learning model will be used to extract image features and predicts the class and scientific name of the flower. The identification of flower name from an input image is based on RGB histogram data. The researchers found that the proposed system is able to classify flower images with an accuracy of 80.67%. The training set consists of 17 different types of flower species. In the proposed model we use feature extraction algorithm to extract features.
Pages: 358-360  |  56 Views  5 Downloads
How to cite this article:
G Sai Sirisha, I Mounika, K Mukesh and B Dharani. Image classification using machine learning. International Journal of Applied Research. 2020; 6(5): 358-360.
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International Journal of Applied Research