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Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops

Author

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  • Aqeel Iftikhar Jajja

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45500, Pakistan)

  • Assad Abbas

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 45500, Pakistan)

  • Hasan Ali Khattak

    (School of Electrical Engineering & Computer Science (SEECS), National University of Sciences & Technology (NUST), H12, Islamabad 44000, Pakistan)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

  • Abbas Khalid

    (Department of Computer Science and IT, The University of Lahore, Lahore 54590, Pakistan)

  • Hafiz Tayyab Rauf

    (Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST18 0YB, UK)

  • Sebastian Kujawa

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.

Suggested Citation

  • Aqeel Iftikhar Jajja & Assad Abbas & Hasan Ali Khattak & Gniewko Niedbała & Abbas Khalid & Hafiz Tayyab Rauf & Sebastian Kujawa, 2022. "Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops," Agriculture, MDPI, vol. 12(10), pages 1-17, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1529-:d:922799
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    References listed on IDEAS

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    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
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    Cited by:

    1. Mahnoor Khalid & Muhammad Shahzad Sarfraz & Uzair Iqbal & Muhammad Umar Aftab & Gniewko Niedbała & Hafiz Tayyab Rauf, 2023. "Real-Time Plant Health Detection Using Deep Convolutional Neural Networks," Agriculture, MDPI, vol. 13(2), pages 1-26, February.

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