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Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)

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Advanced Informatics for Computing Research (ICAICR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

Abstract

The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.

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Acknowledgments

The authors are much obliged to the Department of Computer Science and Engineering, Manipal Institute of Technology - Manipal Academy of Higher Education, Manipal, INDIA-576104, for providing outstanding lab facilities to succeed in this work.

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Authors and Affiliations

  1. Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India

    V. G. Narendra & K. Govardhan Hegde

Authors
  1. V. G. Narendra
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  2. K. Govardhan Hegde
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Corresponding author

Correspondence to K. Govardhan Hegde .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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Narendra, V.G., Govardhan Hegde, K. (2019). Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_34

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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