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Research on Cotton Impurity Detection Algorithm Based on Image Segmentation

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Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12736))

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Abstract

With the widespread application of computer and multimedia technology in the agricultural field, problems such as complex workloads have been effectively resolved, contributing to the promotion of agricultural information, modernization, industrialization, and intelligence. The impurity content of cotton seriously affects its quality and price. Thus, how to effectively detect the impurity rate has become a difficult problem for the cotton processing industry. From the perspective of computer image segmentation, this paper performs color segmentation, maximum entropy segmentation, and level set segmentation on the image library, and compares the performance of the algorithms. Experiments demonstrate that the image segmentation algorithm is more accurate and robust compared with manual detection.

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Funding

The authors received no specific funding for this study.

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Authors and Affiliations

  1. ChongQing University, ChongQing, 400044, China

    Haolong Yang & Chunqiang Hu

  2. Zhejiang Dongfang Polytechinc College, Wenzhou, 325000, China

    Qi Diao

Authors
  1. Haolong Yang
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  2. Chunqiang Hu
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  3. Qi Diao
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Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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The authors declare that they have no conflicts of interest to report regarding the present study.

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Yang, H., Hu, C., Diao, Q. (2021). Research on Cotton Impurity Detection Algorithm Based on Image Segmentation. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-78609-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78608-3

  • Online ISBN: 978-3-030-78609-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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