Mathematical biology has emerged as a powerful interdisciplinary approach for analyzing complex plant systems through quantitative modeling and computational techniques. This study presents an integrated framework that combines plant growth dynamics, ecological interactions, and genetic mechanisms to provide a comprehensive understanding of plant behavior. The research employs differential equation–based models, ecological competition models, and population genetics principles to examine how plants grow, interact within ecosystems, and evolve over time. The logistic growth model is utilized to describe plant growth under resource limitations, demonstrating the characteristic sigmoidal pattern and the role of carrying capacity in regulating biomass. Ecological interactions are analyzed using competition models, revealing conditions for species coexistence and competitive exclusion. Genetic variation is examined through population genetics models, highlighting how allele frequencies change under selection pressure and contribute to plant adaptation and evolution. The integration of these components illustrates the interdependence of growth, ecology, and genetics in shaping plant systems.The study relies on secondary data and computational simulations to validate the proposed models, with results showing strong agreement with observed biological patterns. Sensitivity analysis further emphasizes the importance of key parameters such as growth rate, competition coefficients, and genetic fitness in influencing system behavior. While the models provide valuable insights, certain limitations related to simplifying assumptions and environmental variability are acknowledged. Overall, this research underscores the significance of mathematical biology in advancing plant science by offering predictive and analytical tools for understanding complex biological processes.
Ramulu et al. (Mon,) studied this question.