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A CNNA-Based Lightweight Multi-Scale Tomato Pest and Disease Classification Method

Author

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  • Yanlei Xu

    (College of Information and Technology, JiLin Agricultural University, Changchun 130118, China)

  • Zhiyuan Gao

    (College of Information and Technology, JiLin Agricultural University, Changchun 130118, China)

  • Yuting Zhai

    (College of Information and Technology, JiLin Agricultural University, Changchun 130118, China)

  • Qi Wang

    (College of Information and Technology, JiLin Agricultural University, Changchun 130118, China)

  • Zongmei Gao

    (Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, Washington State University, Prosser, WA 99350, USA)

  • Zhao Xu

    (Non Commissioned Officer School, Army Academy of Armored Force, Changchun 130118, China)

  • Yang Zhou

    (College of Information and Technology, JiLin Agricultural University, Changchun 130118, China)

Abstract

Tomato is generally cultivated by transplanting seedlings in ridges and furrows. During growth, there are various types of tomato pests and diseases, making it challenging to identify them simultaneously. To address this issue, conventional convolutional neural networks have been investigated, but they have a large number of parameters and are time-consuming. In this paper, we proposed a lightweight multi-scale tomato pest and disease classification network, called CNNA. Firstly, we constructed a dataset of tomato diseases and pests consisting of 27,193 images with 18 categories. Then, we compressed and optimized the ConvNeXt-Tiny network structure to maintain accuracy while significantly reducing the number of parameters. In addition, we proposed a multi-scale feature fusion module to improve the feature extraction ability of the model for different spot sizes and pests, and we proposed a global channel attention mechanism to enhance the sensitivity of the network model to spot and pest features. Finally, the model was trained and deployed to the Jetson TX2 NX for inference of tomato pests and diseases in video stream data. The experimental results showed that the proposed CNNA model outperformed the pre-trained lightweight models such as MobileNetV3, MobileVit, and ShuffleNetV2 in terms of accuracy and all parameters, with a recognition accuracy of 98.96%. Meanwhile, the error rate, inference time for a single image, network parameters, FLOPs, and model size were only 1%, 47.35 ms, 0.37 M, 237.61 M, and 1.47 MB, respectively.

Suggested Citation

  • Yanlei Xu & Zhiyuan Gao & Yuting Zhai & Qi Wang & Zongmei Gao & Zhao Xu & Yang Zhou, 2023. "A CNNA-Based Lightweight Multi-Scale Tomato Pest and Disease Classification Method," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8813-:d:1159481
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    References listed on IDEAS

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    1. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    2. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
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