Optimization of Crop Planting Strategies Based on Monte Carlo Model under Dynamic Multifactor Analysis

Abstract

Based on crop statistics, this paper uses Monte Carlo intelligent algorithms to optimize crop cultivation between 2024 and 2030, aiming to develop a cultivation strategy that maximizes returns. First, this paper checked the completeness and accuracy of the data through data preprocessing, dealt with the stagnant sales situation in which the total crop production exceeded the expected sales volume, and categorized it into two scenarios of wastage and selling at a reduced price. Then, Monte Carlo Intelligent Algorithm was applied for crop planting strategy optimization. Further, the sales volume, mu yield, planting cost and volatility of sales price of each type of crops were added as constraints to enhance the realistic adaptability of the model. In addition, this paper quantified the linear relationship between the expected crop sales volume, sales price and planting cost using Spearman's phase relation, and further constructed a dynamic multifactorial crop planting optimization model, which considered the substitutability and complementarity of crops.