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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Volume 7 - 2024 | doi: 10.3389/frai.2024.1384709
This article is part of the Research Topic Defining the Role of Artificial Intelligence (AI) in the Food Sector and its Applications View all 4 articles

Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania

Provisionally accepted
Flavia S. Mayo Flavia S. Mayo 1*Neema Mduma Neema Mduma 1Mvurya Mgala Mvurya Mgala 2Ciira Maina Ciira Maina 3
  • 1 Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania
  • 2 Technical University of Mombasa, Mombasa, Kenya
  • 3 Dedan Kimathi University of Technology, Nyeri, Kenya

The final, formatted version of the article will be published soon.

    Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1% and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

    Keywords: Deep learning models, Maize diseases, Early detection, Convolutional Neural Network, vision Transformer, Maize streak virus, Maize lethal necrosis

    Received: 10 Feb 2024; Accepted: 02 Aug 2024.

    Copyright: © 2024 Mayo, Mduma, Mgala and Maina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Flavia S. Mayo, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.