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Improved Lightweight Mango Sorting Model Based on Visualization

Author

Listed:
  • Hongyu Wei

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Wenyue Chen

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Lixue Zhu

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Xuan Chu

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Hongli Liu

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Yinghui Mu

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

  • Zhiyu Ma

    (College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

Abstract

Neural networks are widely used in fruit sorting and have achieved some success. However, due to the limitations of storage space and power consumption, the storage and computing of a neural network model on embedded devices remain a massive challenge. Aiming at realizing a lightweight mango sorting model, the feature-extraction characteristics of the shallow and deep networks of the SqueezeNet model were analyzed by a visualization method, and then eight lightweight models were constructed by removing redundant layers or modifying the convolution kernel. It was found that the model designated Model 4 performed well after training and testing. The class activation mapping method was used to explain the basis of the classification decision, and the model was compared with ten classical classification models. The results showed that the calculation performance of the model was significantly improved without reducing accuracy. The parameter storage requirement is 0.87 MB, and the calculation amount is 181 MFLOPS, while the average classification accuracy can still be maintained at 95.64%. This model has a high-cost performance and can be widely used in embedded devices.

Suggested Citation

  • Hongyu Wei & Wenyue Chen & Lixue Zhu & Xuan Chu & Hongli Liu & Yinghui Mu & Zhiyu Ma, 2022. "Improved Lightweight Mango Sorting Model Based on Visualization," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1467-:d:914562
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    References listed on IDEAS

    as
    1. Veeranjaneya Reddy Lebaka & Young-Jung Wee & Weibing Ye & Mallikarjuna Korivi, 2021. "Nutritional Composition and Bioactive Compounds in Three Different Parts of Mango Fruit," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    2. Inmaculada Barranco-Chamorro & Rosa M. Carrillo-García, 2021. "Techniques to Deal with Off-Diagonal Elements in Confusion Matrices," Mathematics, MDPI, vol. 9(24), pages 1-22, December.
    3. Bin Zheng & Tao Huang, 2021. "Mango Grading System Based on Optimized Convolutional Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

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