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.
Abbreviations
- BR-BNN:
Bayesian regularization backpropagation neural network
- RKM-4:
Fourth order Runge–Kutta method
- MSE:
Mean square error
- ANNs:
Artificial neural networks
- ML:
Machine learning
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Jan, H., Sulaiman, M., Khan, M.F. et al. Predictive modeling of pest spread in tea plants using an intelligent computational approach.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-53210-2
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DOI: https://doi.org/10.1038/s41598-026-53210-2
Keywords
- Tri-trophic model
- Tea plants
- Mathematical modeling
- Machine learning
- Optimization technique
- Bayesian regularization backpropagation neural network
Source: Ecology - nature.com
