Plant growth is a complex biological process influenced by environmental conditions and resource availability. This study develops a mathematical framework to model plant height using linear, exponential, and logistic growth models. A regression model is used to study the effect of water, fertilizer, and sunlight, and a constrained optimization problem is formulated to maximize plant height. To incorporate real-world variability, the model is extended to a stochastic differential equation under climate uncertainty. A case study based on ICAR growth-stage data is used to validate the model. Graphical analysis and optimization results demonstrate that optimal resource allocation significantly improves plant growth. The study highlights the importance of mathematical foundations and computational techniques in emerging technologies such as precision agriculture and smart farming.
Dr.M.Archana (Thu,) studied this question.