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Progress in Intelligent Computing Techniques: Theory, Practice, and Applications pp 165–172Cite as

Defect Identification for Simple Fleshy Fruits by Statistical Image Feature Detection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 518))

Abstract

Maintaining the product quality of the fleshy fruits is the important criterion in the market. Quality assessment with computer vision techniques is possible with the proper selection of classifier which will give an optimal classification. Feature extraction is done in two steps: (1) Fruit image features were extracted using the 2-level discrete wavelet transform. (2) Statistical parameters like Mean and Variance of discrete wavelet transform features were calculated. A Feed-Forward back propagation neural classifier performed superior than the Support Vector Machine Linear classifier for identifying into three classes (Best, Average, and Poor) by achieving overall good accuracy.

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Authors and Affiliations

  1. MIT Academy of Engineering, Alandi (D), Pune, Maharashtra, India

    Smita

  2. International Institute of Information Technology, Hinjawadi, Pune, Maharashtra, India

    Varsha Degaonkar

Authors
  1. Smita
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  2. Varsha Degaonkar
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Corresponding author

Correspondence to Smita .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Dept. of Computer Science & Engineering National Institute of Technology, Rourkela, Odisha, India

    Pankaj Kumar Sa

  2. National Institute of Technology, Dept. of Computer Science & Engineering National Institute of Technology, Rourkela, Odisha, India

    Manmath Narayan Sahoo

  3. Universiti Malaysia Perlis (UniMAP), School of Mecahtronics Engineering Universiti Malaysia Perlis (UniMAP), Arau, Perlis, Malaysia

    M. Murugappan

  4. The University of Exeter, Lecturer The University of Exeter, Exeter, Devon, United Kingdom

    Yulei Wu

  5. National Institute of Technology, Dept. of Computer Science & Engineering National Institute of Technology, Rourkela, Odisha, India

    Banshidhar Majhi

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© 2018 Springer Nature Singapore Pte Ltd.

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Smita, Degaonkar, V. (2018). Defect Identification for Simple Fleshy Fruits by Statistical Image Feature Detection. In: Sa, P., Sahoo, M., Murugappan, M., Wu, Y., Majhi, B. (eds) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Advances in Intelligent Systems and Computing, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-10-3373-5_16

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  • DOI: https://doi.org/10.1007/978-981-10-3373-5_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3372-8

  • Online ISBN: 978-981-10-3373-5

  • eBook Packages: EngineeringEngineering (R0)

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