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Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan

    Marriam Nawaz

  2. Faculty of Computing, Department of Computer Science, Riphah International University Gulberg Green Campus, Islamabad, Pakistan

    Tahira Nazir

  3. Department of Computer Science, HITEC University, Taxila, Pakistan

    Muhammad Attique Khan

  4. Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, 602105, India

    Venkatesan Rajinikanth

  5. Department of Applied Data Science, Noroff University College, 4612, Kristiansand, Norway

    Seifedine Kadry

  6. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates

    Seifedine Kadry

  7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Seifedine Kadry

Authors
  1. Marriam Nawaz
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  2. Tahira Nazir
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  3. Muhammad Attique Khan
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  4. Venkatesan Rajinikanth
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  5. Seifedine Kadry
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Corresponding authors

Correspondence to Muhammad Attique Khan or Seifedine Kadry .

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Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Nawaz, M., Nazir, T., Khan, M.A., Rajinikanth, V., Kadry, S. (2023). Plant Disease Classification Using VGG-19 Based Faster-RCNN. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_23

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-37940-6

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