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A Novel Hybrid Deep Learning Model for Crop Disease Detection Using BEGAN

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Ubiquitous Networking (UNet 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13853))

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Abstract

Crop diseases are a considerable threat in the agricultural sector as they adversely affect the production and quality of agricultural products, resulting in heavy economic losses for both farmers and the country. Therefore, early identification and diagnosis of crop diseases at each stage of their lifespan is critical to protect and maximize crop yields. In this paper, we have proposed a novel deep learning model that utilizes the began to generate synthetic images of crop leaves in order to improve the network generalizability. Thereafter, a hybrid InceptionV3 + RF model is trained on real and synthetic images using transfer learning to classify crop leaves images in ten categories.

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Acknowledgment

This work was done within the framework “Agrometeorological Stations Platform” project funded by the Moroccan Ministry of Higher Education and Scientific Research - National Centre for Scientific and Technical Research (NCSTR) (PPR2 project).

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Authors and Affiliations

  1. Department of Electrical Engineering, NEST Research Group ENSEM, Hassan II University, Casablanca, Morocco

    Houda Orchi, Mohamed Sadik & Mohammed Khaldoun

Authors
  1. Houda Orchi
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  2. Mohamed Sadik
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  3. Mohammed Khaldoun
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Corresponding author

Correspondence to Houda Orchi .

Editor information

Editors and Affiliations

  1. University of Quebec at Montreal, Montreal, QC, Canada

    Essaid Sabir

  2. University of Quebec at Montreal, Montreal, QC, Canada

    Halima Elbiaze

  3. Public University of Navarre, Pamplona, Spain

    Francisco Falcone

  4. University of Quebec at Montreal, Montreal, QC, Canada

    Wessam Ajib

  5. Hassan II University of Casablanca, Casablanca, Morocco

    Mohamed Sadik

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Orchi, H., Sadik, M., Khaldoun, M. (2023). A Novel Hybrid Deep Learning Model for Crop Disease Detection Using BEGAN. In: Sabir, E., Elbiaze, H., Falcone, F., Ajib, W., Sadik, M. (eds) Ubiquitous Networking. UNet 2022. Lecture Notes in Computer Science, vol 13853. Springer, Cham. https://doi.org/10.1007/978-3-031-29419-8_20

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  • DOI: https://doi.org/10.1007/978-3-031-29419-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29418-1

  • Online ISBN: 978-3-031-29419-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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