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Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism

Author

Listed:
  • Peng Wang

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Jiang Liu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Lijia Xu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Peng Huang

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Xiong Luo

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Yan Hu

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Zhiliang Kang

    (College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China)

Abstract

The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with attention mechanism model based on transfer learning to realize the classification of Amanita . When the model is trained, the weight on ImageNet is used for pre-training, and the Adam optimizer is used to update network parameters. In the test process, images of Amanita at different growth stages were used to further test the generalization ability of the model. After comparing our model with other models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (95.2%) and has good generalization ability. It is an efficient classification model, which provides a new option for mushroom classification in areas with limited computing resources.

Suggested Citation

  • Peng Wang & Jiang Liu & Lijia Xu & Peng Huang & Xiong Luo & Yan Hu & Zhiliang Kang, 2021. "Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:393-:d:543480
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    References listed on IDEAS

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    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
    2. Suk-Ju Hong & Sang-Yeon Kim & Eungchan Kim & Chang-Hyup Lee & Jung-Sup Lee & Dong-Soo Lee & Jiwoong Bang & Ghiseok Kim, 2020. "Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors," Agriculture, MDPI, vol. 10(5), pages 1-12, May.
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    Cited by:

    1. Yan Hu & Lijia Xu & Peng Huang & Xiong Luo & Peng Wang & Zhiliang Kang, 2021. "Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning," Agriculture, MDPI, vol. 11(11), pages 1-19, November.
    2. Xiong Luo & Lijia Xu & Peng Huang & Yuchao Wang & Jiang Liu & Yan Hu & Peng Wang & Zhiliang Kang, 2021. "Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods," Agriculture, MDPI, vol. 11(7), pages 1-15, July.

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