An Artificial Intelligence Framework for Disease Detection in Potato Plants

Authors

  • Ahmed Abbas University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Umair Maqsood University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Saif Ur Rehman University Institute of Information Technology, PMAS Arid Agriculture University, Pakistan
  • Khalid Mahmood Institute of Computing and Information Technology, Gomal University, Pakistan
  • Tahani AlSaedi Applied College, Taibah University, Saudi Arabia
  • Mahwish Kundi University of Leicester, United Kingdom
Volume: 14 | Issue: 1 | Pages: 12628-12635 | February 2024 | https://doi.org/10.48084/etasr.6456

Abstract

Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively.

Keywords:

deep learning, disease classification, CNN, potato disease detection, image processing, machine vision, smart sprayer

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How to Cite

[1]
A. Abbas, U. Maqsood, S. Ur Rehman, K. Mahmood, T. AlSaedi, and M. Kundi, “An Artificial Intelligence Framework for Disease Detection in Potato Plants”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12628–12635, Feb. 2024.

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