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A Model for Detecting Xanthomonas campestris Using Machine Learning Techniques Enhanced by Optimization Algorithms

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  • Daniel-David Leal-Lara

    (Computer and Systems Engineering Program, Faculty of Engineering and Basic Sciences, Fundación Universitaria Los Libertadores, Bogotá 111221, Colombia
    Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá 111611, Colombia)

  • Julio Barón-Velandia

    (Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá 111611, Colombia)

  • Lina-María Molina-Parra

    (Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá 111611, Colombia)

  • Ana-Carolina Cabrera-Blandón

    (Computer and Systems Engineering Program, Faculty of Engineering and Basic Sciences, Fundación Universitaria Los Libertadores, Bogotá 111221, Colombia)

Abstract

The bacterium Xanthomonas campestris poses a significant threat to global agriculture due to its ability to infect leaves, fruits, and stems under various climatic conditions. Its rapid spread across large crop areas results in economic losses, compromises agricultural productivity, increases management costs, and threatens food security, especially in small-scale agricultural systems. To address this issue, this study developed a model that combines fuzzy logic and neural networks, optimized with intelligent algorithms, to detect symptoms of this foliar disease in 15 essential crop species under different environmental conditions using images. For this purpose, Sugeno-type fuzzy inference systems and adaptive neuro-fuzzy inference systems (ANFIS) were employed, configured with rules and clustering methods designed to address cases where diagnostic uncertainty arises due to the imprecision of different agricultural scenarios. The model achieved an accuracy of 93.81%, demonstrating robustness against variations in lighting, shadows, and capture angles, and proving effective in identifying patterns associated with the disease at early stages, enabling rapid and reliable diagnoses. This advancement represents a significant contribution to the automated detection of plant diseases, providing an accessible tool that enhances agricultural productivity and promotes sustainable practices in crop care.

Suggested Citation

  • Daniel-David Leal-Lara & Julio Barón-Velandia & Lina-María Molina-Parra & Ana-Carolina Cabrera-Blandón, 2025. "A Model for Detecting Xanthomonas campestris Using Machine Learning Techniques Enhanced by Optimization Algorithms," Agriculture, MDPI, vol. 15(3), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:223-:d:1572143
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    References listed on IDEAS

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    1. Sarlaki, Ehsan & Sharif Paghaleh, Ali & Kianmehr, Mohammad Hossein & Asefpour Vakilian, Keyvan, 2021. "Valorization of lignite wastes into humic acids: Process optimization, energy efficiency and structural features analysis," Renewable Energy, Elsevier, vol. 163(C), pages 105-122.
    2. Esmaili, Maryam & Aliniaeifard, Sasan & Mashal, Mahmoud & Vakilian, Keyvan Asefpour & Ghorbanzadeh, Parisa & Azadegan, Behzad & Seif, Mehdi & Didaran, Fardad, 2021. "Assessment of adaptive neuro-fuzzy inference system (ANFIS) to predict production and water productivity of lettuce in response to different light intensities and CO2 concentrations," Agricultural Water Management, Elsevier, vol. 258(C).
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    Cited by:

    1. Changyong Li & Shunchun Zhang & Zhijie Ma, 2025. "RF-YOLOv7: A Model for the Detection of Poor-Quality Grapes in Natural Environments," Agriculture, MDPI, vol. 15(4), pages 1-16, February.

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