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A Real-Time Shrimp with and without Shells Recognition Method for Automatic Peeling Machines Based on Tactile Perception

Author

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  • Xueshen Chen

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Yuesong Xiong

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Peina Dang

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Chonggang Tao

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Changpeng Wu

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Enzao Zhang

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Tao Wu

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

Abstract

Accurate and automatic real-time recognition of shrimp with and without shells is the key to improve the efficiency of automatic peeling machines and reduce the labor cost. Existing methods cannot obtain excellent accuracy in the absence of target samples because there are too many species of shrimp to obtain a complete dataset. In this paper, we propose a tactile recognition method with universal applicability. First, we obtained tactile data, e.g., the texture and hardness of the surface of the shrimp, through a novel layout using the same type of sensors, and constructed fusion features based on the energy and nonstationary volatility (ENSV). Second, the ENSV features were input to an adaptive recognition boundary model (ARBM) for training to obtain the recognition boundary of shrimp with and without shells. Finally, the effectiveness of the proposed model was verified by comparison with other tactile models. The method was tested with different species of shrimp and the results were 88.2%, 87.0%, and 89.4%, respectively. The recognition accuracy of the overall, shrimp with shells and shrimp without shells verified the generalizability of the proposed method. This method can help to improve the efficiency of automatic peeling machines and reduce the labor cost.

Suggested Citation

  • Xueshen Chen & Yuesong Xiong & Peina Dang & Chonggang Tao & Changpeng Wu & Enzao Zhang & Tao Wu, 2023. "A Real-Time Shrimp with and without Shells Recognition Method for Automatic Peeling Machines Based on Tactile Perception," Agriculture, MDPI, vol. 13(2), pages 1-14, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:422-:d:1064894
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    References listed on IDEAS

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    1. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
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    Cited by:

    1. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.

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