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Method of Attention-Based CNN for Weighing Pleurotus eryngii

Author

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  • Junmin Jia

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Fei Hu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Xubo Zhang

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Zongyou Ben

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Yifan Wang

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Kunjie Chen

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

Automatic weight detection is an essential step in the factory production of Pleurotus eryngii . In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R 2 , RMSE , MAE , and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii .

Suggested Citation

  • Junmin Jia & Fei Hu & Xubo Zhang & Zongyou Ben & Yifan Wang & Kunjie Chen, 2023. "Method of Attention-Based CNN for Weighing Pleurotus eryngii," Agriculture, MDPI, vol. 13(9), pages 1-14, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1728-:d:1229786
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    References listed on IDEAS

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    1. Jaesu Lee & Haseeb Nazki & Jeonghyun Baek & Youngsin Hong & Meonghun Lee, 2020. "Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture," Sustainability, MDPI, vol. 12(21), pages 1-15, November.
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    Cited by:

    1. Mingjie Wu & Xuanxi Yang & Lijun Yun & Chenggui Yang & Zaiqing Chen & Yuelong Xia, 2024. "A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model," Agriculture, MDPI, vol. 14(8), pages 1-24, August.

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