Research on Crop Planting Strategies Based on Monte Carlo Simulation and Greedy Algorithm

Based on the methods of greedy algorithm and Monte Carlo simulation, this paper explores the optimal planting scheme under different circumstances, aiming to optimise the crop planting structure. Firstly, the economic indicator characteristics of crops are extracted, the corresponding profit function is constructed, and the objective function is determined under the constraints of different land types and crop rotation requirements. Secondly, considering the uncertainty and potential risk of production and marketing characteristics, Monte Carlo simulation is used to further enhance the robustness of the model and optimise the planting strategy, resulting in a robust planting scheme that can maintain high profits under different circumstances. Finally, a matrix of correlation coefficients is constructed to explore the substitutability and complementarity among crops and to analyse their economic relationships from both planting and marketing perspectives. This study provides practical theoretical basis and methodological support for agricultural planting decision-making, and helps to better adapt to changes in crop planting demand.