Fusarium Wilt of Banana (FWB), caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense, remains a major threat to global banana production due to its persistence in soil and lack of effective treatment. This study develops and analyzes a novel deterministic Susceptible-Exposed-Infectious-Removed-Pathogen (SEIR-P) compartmental model that explicitly incorporates environmental pathogen dynamics alongside plant infection stages. The inclusion of the pathogen compartment is biologically justified by the long-term survival of fungal spores in soil and their role in FWB transmission. Unlike existing models, the proposed framework integrates three time-dependent control strategies: use of resistant plants \(u_1\) , removal of infected plants \(u_2\) , and sanitation \(u_3\) , within an optimal control setting to evaluate their combined epidemiological and economic impact. Model parameters were obtained from literature, and further evaluated using field data from a commercial banana plantation in northern Mozambique. The fitted model accurately captures the observed infection patterns, supporting its ability to capture realistic disease dynamics. The effective reproduction number \(\mathcal {R}_e\) and global stability of the disease-free equilibrium are derived to characterize transmission dynamics. A global sensitivity analysis based on Latin Hypercube Sampling (RHS) and Partial Rank Correlation Coefficients (PRCC) identifies key drivers of disease spread, particularly the recruitment of susceptible plants and pathogen-mediated transmission. An optimal control problem is formulated using Pontryagin’s Maximum Principle to determine a control strategy that minimizes both infection burden and intervention costs. Numerical simulations evaluate three control strategies and their combinations. The results demonstrate that while individual strategies like roguing ( \(u_2\) ) can be effective at low levels ( \(\approx \) 10%), a combined approach integrating all three controls ( \(u_1, u_2, u_3\) ) yields the most robust and minimal cost outcome. This integrated strategy achieves the lowest total implementation cost \(\left( \approx 8.86 \times 10^{3}\right) \ \text {USD}\) representing the cumulative intervention cost over the simulation period, showcasing a strong synergistic effect. This study provides a powerful quantitative framework for policymakers and farmers, demonstrating that an upfront, integrated intervention is not only epidemiologically superior but also the most economically prudent long term strategy for managing this devastating disease.
