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A Machine Learning Driven Android Based Mobile Application for Flower Identification

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Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

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Abstract

In the field of Botany the research of flower classification scheme is an extremely significant topic. A classifier of flowers by maximum precision will also carry numerous enjoyments to human lives. However, there are tranquil a few disclaim in the identification of flower images due to the multipart conditions of flowers, the resemblance connecting the unusual flowers of species, and the variations surrounded by the similar species of flowers. The classification of flower is largely depend on the Color, shape and texture features which needs populace to choose features for classification and the accurateness is not extremely high. We were designed an Android application using machine learning techniques for flower identification. In this paper, based on Image Net model of DNN Tensor Flow Framework platform, to get better the accuracy of flower classification significantly, the Deep Neural Network (DNN) knowledge were used to retrain the flower category datasets. We were used ten category datasets. The accuracy of Image Net based MobileBetV2 model was 98.47% and proposed Deep CNN Model accuracy was 89.87% in our result. Any user can identify the flower by using our application from the flower images.

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Acknowledgement

This work was supported by the Sunway University Research Grant (GRTIN-IRG-05-2021). The authors also express their gratitude to the Department of Computer Science and Engineering, BGC Trust University Bangladesh.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong, 4301, Bangladesh

    Towhidul Islam & Nurul Absar

  2. Center for Data Analytics Research (CeDAR), ADA University Baku, Baku, Azerbaijan

    Abzetdin Z. Adamov

  3. Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway, 47500, Selangor, Malaysia

    Mayeen Uddin Khandaker

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  1. Towhidul Islam
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  2. Nurul Absar
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  3. Abzetdin Z. Adamov
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  4. Mayeen Uddin Khandaker
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Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Islam, T., Absar, N., Adamov, A.Z., Khandaker, M.U. (2021). A Machine Learning Driven Android Based Mobile Application for Flower Identification. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-82269-9_13

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