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BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification

Author

Listed:
  • Jianping Wang

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Zhiyu Li

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Guohong Gao

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Yan Wang

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Chenping Zhao

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Haofan Bai

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Yingying Lv

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Xueyan Zhang

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Qian Li

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

Abstract

With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F 1-score, and parameters. It holds significant promise for applications in strawberry disease identification.

Suggested Citation

  • Jianping Wang & Zhiyu Li & Guohong Gao & Yan Wang & Chenping Zhao & Haofan Bai & Yingying Lv & Xueyan Zhang & Qian Li, 2024. "BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification," Agriculture, MDPI, vol. 14(5), pages 1-25, April.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:665-:d:1382418
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    References listed on IDEAS

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    1. Haram Kim & Dongsoo Kim, 2023. "Deep-Learning-Based Strawberry Leaf Pest Classification for Sustainable Smart Farms," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
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    Cited by:

    1. Dongcheng Li & Yongqi Xu & Zheming Yuan & Zhijun Dai, 2024. "GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition," Agriculture, MDPI, vol. 14(11), pages 1-22, October.

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