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Deep transfer learning-based automated detection of blast disease in paddy crop

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Abstract

A major proportion of the loss faced by the agricultural industry originates from the diseases of the crop during cultivation. Paddy crop is one of the dominant crops which provides food to a huge population. In this crop, the losses caused by such diseases vary from 30 to 90% of the yield. Therefore, the automated detection of different diseases in paddy crops seeks the attention of the research community. In this context, the present work proposes a deep transfer learning solution for the automated detection of blast disease of paddy, which is the major cause of its yield reduction. For this purpose, an image dataset of healthy and blast disease-infected leave images of paddy crop has been developed. These images are fed to five convolutional neural network-based deep transfer learning algorithms, viz., LeNet, AlexNet, VGG 16, Inception v1, and Xception models for binary classification. The performance analysis of given algorithms reveals that AlexNet provides better results for binary classification with an average accuracy of 98.7% followed by VGG 16 and LeNet architectures having accuracies of 98.2% and 97.8%. So, this deep transfer learning-based approach may assist in reducing the gap between experts and farmers by providing an automated expert advice platform for the timely detection of diseases in paddy crop.

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Data availability

The dataset used in this research work is developed by researchers on their own and can be made available on request for appropriate future work.

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Acknowledgements

This work has been supported by the Department of Electronics Technology and Department of Agriculture, Guru Nanak Dev University, Amritsar, Punjab (India), by providing excellent laboratories for the research work.

Funding

This work has not received any funding from any public/private organization.

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Authors and Affiliations

  1. Department of Agriculture, Guru Nanak Dev University, Amritsar, India

    Amandeep Singh

  2. Department of Electronics Technology, Guru Nanak Dev University, Amritsar, India

    Jaspreet Kaur, Kuldeep Singh & Maninder Lal Singh

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  2. Jaspreet Kaur
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  3. Kuldeep Singh
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Contributions

AS implemented and modified the deep learning architectures for binary classification, performed the experimental analysis and collected the data, contributed to the interpretation of the results, wrote and revised sections of the manuscript. JK conducted experiments using different input sizes for blast disease detection, analyzed the performance of deep transfer learning-based CNNs, contributed to the evaluation of the results using the confusion matrix and ROC curves, assisted in the interpretation of the findings. KS reviewed and refined the experimental setup, conducted statistical analysis of the results, contributed to the interpretation and discussion of the findings. MLS assisted in the interpretation and discussion of the results, reviewed and revised sections of the manuscript. All authors reviewed the manuscript.

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Correspondence to Jaspreet Kaur.

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Singh, A., Kaur, J., Singh, K. et al. Deep transfer learning-based automated detection of blast disease in paddy crop. SIViP 18, 569–577 (2024). https://doi.org/10.1007/s11760-023-02735-4

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