Research on Crop Planting Strategy Based on Monte Carlo Simulation and Genetic Algorithm

Along with the gradual progress of urbanization, rural areas are facing challenges such as limited land resources, market fluctuations, and environmental changes, and how to maximize economic benefits by optimizing crop planting structure and increasing yields has become a key issue in current agricultural development. In this study, we first established a planning model for the target area by optimizing planting strategies and introducing relevant constraints such as land, climate, and market. Subsequently, Monte Carlo simulation was used to generate random samples to simulate crop planting scenarios under different scenarios. The model was optimized by the Pareto multi-objective genetic algorithm to find out the best trade-off between profit maximization and risk minimization. Finally, the optimal solution was solved using the ideal point method. The analysis results show that the scenario has good applicability in the target area, and the expected annual profit can reach 2.17 million yuan, which has a significant economic reference value.