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AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases

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Abstract

This paper introduces AI-PotatoGuard, an artificial intelligence (AI) tool which enhances the management of diseases in potatoes through the use of generative models and convolutional neural networks (CNN). In contrast to traditional practices, AI-PotatoGuard is a tool which provides the ability to detect potatoes in the early stages of the disease and also precisely detects the area affected. Through AI-PotatoGuard, it was observed that the conventional approach of identifying the diseases have been surpassed with 95% success observed in terms of getting the detection perfectly right and 85% in terms of getting the detection right at a much earlier stage. Traditional practices lagged with 75% detection right observation and a mere 50% in terms of detecting the disease early on. While traditional methods applied chemicals 2–3 times in practice in an area, the monitoring with AI-PotatoGuard resulted in only 2 out of 6 times in the same area. Hence, efficient and sustainable agriculture is achieved using AI.

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Data Availability

On reasonable request, the corresponding author will provide data supporting the study’s results. The raw data cannot be made public for reasons of confidentiality and privacy. However, researchers who satisfy the requirements for access to confidential data can be given access to aggregated and anonymised data as well as the statistical analysis codes.

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Authors and Affiliations

  1. Department of Mobile Computing and Communication, Engineering College, University of Information Technology and Communication, Baghdad, Iraq

    Ghada Al-Kateb

  2. Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, Baghdad, Iraq

    Maad M. Mijwil

  3. Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

    Mohammad Aljanabi

  4. Department of Computer, College of Education, Al-Iraqia University, Baghdad, Iraq

    Mohammad Aljanabi

  5. Department of System Programming, South Ural State University, Chelyabinsk, Russia

    Mostafa Abotaleb

  6. Department of Statistics, PSG College of Arts & Science, Coimbatore 14, India

    S. R. Krishna Priya

  7. College of Agriculture, Rewa, Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur, MP, 486001, India

    Pradeep Mishra

Authors
  1. Ghada Al-Kateb
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  2. Maad M. Mijwil
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  3. Mohammad Aljanabi
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  4. Mostafa Abotaleb
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  5. S. R. Krishna Priya
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  6. Pradeep Mishra
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Correspondence to Ghada Al-Kateb or Pradeep Mishra.

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Al-Kateb, G., Mijwil, M.M., Aljanabi, M. et al. AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases. Potato Res. (2024). https://doi.org/10.1007/s11540-024-09751-y

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