Investigating HLB control strategies using Genetic Algorithms: A two-orchard model approach with ACP Dispersal

This study focuses on the use of genetic algorithms to optimize control parameters in two potential strategies called mechanical and chemical control, for mitigating the spread of Huanglongbing (HLB) in citrus orchards. By developing a two-orchard model that incorporates the dispersal of the Asian C...

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Published inarXiv.org
Main Authors Hernández, Andrés Anzo, Uvencio José Giménez Mujica, Carlos Hernández Gracidas, José Jacobo Oliveros Oliveros
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.09.2023
Subjects
Citrus fruits
Cost function
Disease control
Dispersion
Genetic algorithms
Mathematical models
Optimization
Orchards
Parameter sensitivity
Sensitivity analysis
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Summary:This study focuses on the use of genetic algorithms to optimize control parameters in two potential strategies called mechanical and chemical control, for mitigating the spread of Huanglongbing (HLB) in citrus orchards. By developing a two-orchard model that incorporates the dispersal of the Asian Citrus Psyllid (ACP), the cost functions and objective function are explored to assess the effectiveness of the proposed control strategies. The mobility of ACP is also taken into account to capture the disease dynamics more realistically. Additionally, a mathematical expression for the global reproduction number (\(R_{0}\)) is derived, allowing for sensitivity analysis of the model parameters when ACP mobility is present. Furthermore, we mathematically express the cost function and efficiency of the strategy in terms of the final size and individual \(R_{0}\) of each patch (i.e., when ACP mobility is absent). The results obtained through the genetic algorithms reveal optimal parameters for each control strategy, providing valuable insights for decision-making in implementing effective control measures against HLB in citrus orchards. This study highlights the importance of optimizing control parameters in disease management in agriculture and provides a solid foundation for future research in developing disease control strategies based on genetic algorithms.
ISSN:2331-8422