Research on the optimization of crop planting strategy under multi-dimensional uncertainty —— Based on Monte Carlo simulation, linear planning and crop correlation analysis

Abstract

Crop cultivation in rural areas faces challenges from diversification, plot constraints, and fluctuating market demands, posing threats to farmers' economic income stability. To address this, this paper proposes a crop planting optimization model based on Genetic Algorithm (GA) for the planning period from 2024 to 2030. This model encodes planting schemes as chromosomes, simulates the natural evolution process, and maximizes economic returns through selection, crossover, and mutation operations. Additionally, nine gradient mutation rate experiments are designed to explore their impact. Meanwhile, optimal schemes are simulated and calculated for situations of crop surplus and price reductions. To address parameter uncertainty, a strategy combining Monte Carlo simulation and Linear Programming (LP) is employed to assess risk correlations and solve for optimal schemes under various scenarios. Experimental results demonstrate that this model outperforms traditional methods in enhancing returns and reducing risks, providing robust support for formulating crop planting strategies.