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

Daniel-David Leal-Lara (), Julio Barón-Velandia, Lina-María Molina-Parra and Ana-Carolina Cabrera-Blandón
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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
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

Agriculture, 2025, vol. 15, issue 3, 1-16

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.

Keywords: Sugeno fuzzy inference algorithm; adaptive neuro-fuzzy inference system ANFIS; Xanthomonas campestris; hybrid intelligent algorithm; digital image processing; pattern recognition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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