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An augmented attention-based lightweight CNN model for plant water stress detection

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Abstract

Recently, deep learning techniques specifically the Convolutional Neural Networks (CNNs) have reported outstanding results from the application for plant water stress detection based on computer vision system compared to other machine learning methods. However, the size of the conventional CNN models is generally too large for its deployment on resource-limited devices such as mobile smartphone or embedded devices. In this study, a lightweight CNN is proposed by incorporating attention mechanism as an augmentation module into the model. The model was trained, validated, and tested using plant images of Setaria grass undergone three water stress treatments. Experimental results show that the proposed method improved the interclass precision, recall, F1-score, and the overall accuracy by more than 9%. Compared to the established lightweight CNN models, the proposed lightweight CNN achieved faster computational time with comparable parameters. In addition, the proposed lightweight model is also efficient when trained on small plant dataset with limited overfitting.

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Acknowledgements

This research was funded by Ministry of Higher Education and Universiti Teknologi Malaysia under research grant no. R.K130000.7843.5F348 and Q.K130000.2543.19H97.

Data and codes availability

All original data and code that support the findings of this study are available at https://github.com/hider11/Lightweight-CNN-water-stress.git

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Authors and Affiliations

  1. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Mohd Hider Kamarudin & Zool Hilmi Ismail

  2. Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Zool Hilmi Ismail

  3. Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400, Selangor, Malaysia

    Noor Baity Saidi

  4. Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan

    Kousuke Hanada

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  1. Mohd Hider Kamarudin
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  2. Zool Hilmi Ismail
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  3. Noor Baity Saidi
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Correspondence to Mohd Hider Kamarudin or Zool Hilmi Ismail.

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Kamarudin, M.H., Ismail, Z.H., Saidi, N.B. et al. An augmented attention-based lightweight CNN model for plant water stress detection. Appl Intell 53, 20828–20843 (2023). https://doi.org/10.1007/s10489-023-04583-8

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