For startups and young companies, there is significant uncertainty and managerial flexibility within a company’s business model, research and development (R&D) processes, and commercialization strategy. These characteristics make early stage companies difficult to value. While the predominant valuation tools used include discounted cash flow and multiples analysis, their fixed assumptions and improper risk adjustment tend to undervalue startups with managerial flexibility, uncertainty, and high growth potential. This thesis utilizes stochastic real options to assist with the valuation process for agricultural technology startups in order to better reflect uncertainty, managerial flexibility, and asymmetric growth that is existent. The stochastic real options are integrated into decision trees to account for uncertainty and the two types of risk, being private and market risk. While this application is used for two case studies of startups in agricultural technology, the method can be applied to different startups with varying scenarios and industries.