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Real-time disease detection on bean leaves from a small image dataset using data augmentation and deep learning methods

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Abstract

Disease detection in agricultural crops plays a pivotal role in ensuring food security and sustainable farming practices. Deep learning models, known for their ability in image analysis, often demand extensive image datasets and annotations to achieve high accuracy. However, in the case of bean crops, the absence of a publicly available dataset has posed a significant challenge for applying deep learning algorithms to accurately predict diseases. Additionally, the manual annotation of images demands substantial time and resources. This paper introduces an innovative approach to tackle these issues. We introduce a solution for real-time disease detection on bean leaves, despite the lack of bean-specific image data. Initially, we generate a small dataset from real images and annotate them. Then, we utilize images from the existing dataset PlantDoc (Singh et al. in: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Association for Computing Machinery, pp 249–253, 2020) from leaves of other plant species. Moreover, to compensate for the limitations of a small image dataset, we employ advanced data augmentation techniques, enriching the training data and enhancing the model’s ability to generalize. Our experimental study shows that data augmentation techniques can improve the accuracy of deep learning methods by up to 37%.

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Acknowledgements

This work has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (project code MIS 5047196).

Funding

This work has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (project code MIS 5047196).

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Author notes

  1. Emmanouil Karantoumanis, Vasileios Balafas and Malamati Louta have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Western Macedonia, Campus ZEP, 50100, Kozani, Greece

    Emmanouil Karantoumanis, Vasileios Balafas, Malamati Louta & Nikolaos Ploskas

Authors
  1. Emmanouil Karantoumanis
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  2. Vasileios Balafas
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  3. Malamati Louta
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  4. Nikolaos Ploskas
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Contributions

EK: Methodology, Formal analysis and investigation, Writing - original draft preparation; VB: Methodology, Formal analysis and investigation, Writing - original draft preparation; LT: Conceptualization, Writing - review and editing, Funding acquisition; NP: Conceptualization, Methodology, Writing - review and editing, Resources, Supervision.

Corresponding author

Correspondence to Nikolaos Ploskas.

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Karantoumanis, E., Balafas, V., Louta, M. et al. Real-time disease detection on bean leaves from a small image dataset using data augmentation and deep learning methods. Soft Comput 28, 12929–12941 (2024). https://doi.org/10.1007/s00500-024-10348-3

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