Predictive modeling of pest spread in tea plants using an intelligent computational approach.

Hamad Jan, Muhammad Sulaiman, Muhammad Fawad Khan, Ghaylen Laouini, Izaz Ur Rahman, Mohammed Abdullah Salman

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Hamad JanDepartment of Mathematics, FP & NS, AWKUM, Mardan, 23200, Pakistan.

Muhammad SulaimanDepartment of Mathematics, FP & NS, AWKUM, Mardan, 23200, Pakistan.

Muhammad Fawad KhanSchool of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia.

Ghaylen LaouiniCollege of Engineering and Technology, American University of the Middle East, 54200, Egaila, Kuwait.

Izaz Ur RahmanFaculty of Computing and Information Technology, Sohar University, Sohar, Oman.

Mohammed Abdullah SalmanDepartment of Cyber Security, Faculty of Engineering and Technology, Ar-Rasheed Smart University, Sana'a, Yemen. dr.msalman@amu.edu.ye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tea is a popular beverage prepared from the leaves of the Camellia sinensis plant, embraced by a good share of the world's population. However, pests and predators threaten its production, which must be controlled without adversely affecting the natural resources. This research presents a mathematical framework for a predatory tri-trophic system that captures the growth of tea plants, pests, and predators. Bayesian Regularization Backpropagation Neural Network (BR-BNN), an intelligent computational approach, is used to derive the output solutions of the presented mathematical model. The Fourth Order Runge-Kutta Method (RKM-4) is utilized to find the target solutions, and different scenarios and parameter variations are employed to determine the model's solutions and assess the stability of BR-BNN. Furthermore, the comparison graphs of BR-BNN solutions with the target solutions also exhibit how well the BR-BNN performs with a minimal percentage of error in all the results. The findings can also help address challenges related to increasing yield, protecting the environment, and increasing the sustainability of tea production.

Indexed as

Camellia sinensisModels, TheoreticalTeaAnimalsBayes TheoremComputer SimulationNeural Networks, ComputerTeaBayesian regularization backpropagation neural networkMachine learningMathematical modelingOptimization techniqueTea plantsTri-trophic model

Identifiers

PMID42243236

DOI10.1038/s41598-026-53210-2

PMCPMC13473542

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