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Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM

Author

Listed:
  • Rui Ma

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China)

  • Jia Wang

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China)

  • Wei Zhao

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China)

  • Hongjie Guo

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China)

  • Dongnan Dai

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China)

  • Yuliang Yun

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Li Li

    (Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China)

  • Fengqi Hao

    (Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China)

  • Jinqiang Bai

    (Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China)

  • Dexin Ma

    (College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
    Intelligent Agriculture Institute, Qingdao Agricultural University, Qingdao 266109, China)

Abstract

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

Suggested Citation

  • Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:11-:d:1009738
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    References listed on IDEAS

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    1. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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    Keywords

    MobileNetV2; CBAM; image classification; maize seeds;
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