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Research on External Quality Inspection Technology of Tropical Fruits Based on Computer Vision

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • Kun Zhang

    (College of Ocean Information Engineering, Hainan Tropical Ocean University
    State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University
    College of Information Science and Technology, Hainan University
    Sanya Key Laboratory of Computer Vision, Hainan Tropical Ocean University)

  • Xiaoyan Chen

    (College of Ocean Information Engineering, Hainan Tropical Ocean University
    Sanya Key Laboratory of Computer Vision, Hainan Tropical Ocean University)

  • Haifeng Wang

    (College of Ocean Information Engineering, Hainan Tropical Ocean University
    State Key Laboratory of Marine Resources Utilization in South China Sea, Hainan University
    Sanya Key Laboratory of Computer Vision, Hainan Tropical Ocean University)

Abstract

With the computer vision technology in the image processing has been widely used, which for the automatic classification of fruit provides a research space. This paper mainly uses the method of computer vision, combined with the problem of grade quality detection of agricultural products in agricultural research hotspots. Taking tropical fruit of Hainan as the research object, taking mango as the experimental object, extracting the characteristics of fruit image, explore the differences in the external size and color of different types of fruits, and establish a visual quality inspection technology for tropical fruits based on computer vision.

Suggested Citation

  • Kun Zhang & Xiaoyan Chen & Haifeng Wang, 2018. "Research on External Quality Inspection Technology of Tropical Fruits Based on Computer Vision," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 165-174, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_18
    DOI: 10.1007/978-3-319-72745-5_18
    as

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