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Neural Network Application to Eggplant Classification

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

Recently there have been developed automatic grading and sorting systems for fruits and vegetables. In this paper, eggplant grading system using image processing and artificial neural network is considered. The lighting conditions are discussed for taking color components of the eggplant image effectively. The shape parameters such as length, girth, etc. are measured using image processing. On the other hand, bruises of the eggplants are detected and classified based on the color information by using artificial neural network. Some experimental results are presented for illustration.

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Author information

Authors and Affiliations

  1. Mitsubishi Electric, Sanda Factory, Sanda, Hyogo, 669-1513, Japan

    Yasuo Saito

  2. Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, 565-0871, Japan

    Toshiharu Hatanaka & Katsuji Uosaki

  3. Technical Research Institute, Hitachi Zosen, Taisho, Osaka, 551-0022, Japan

    Hidekazu Shigeto

Authors
  1. Yasuo Saito
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  2. Toshiharu Hatanaka
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  3. Katsuji Uosaki
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  4. Hidekazu Shigeto
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Saito, Y., Hatanaka, T., Uosaki, K., Shigeto, H. (2003). Neural Network Application to Eggplant Classification. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_128

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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