Research on optimal crop planting strategy based on K-means cluster analysis

This study explores the issue of crop planting strategies in a rural area in North China, which is facing many challenges from traditional agricultural planting models. In response to this situation, this study aims to provide scientific and reasonable crop planting strategies for the village through mathematical modeling to promote rural revitalization and achieve sustainable agricultural development. In formulating the optimal planting strategy for the period from 2024 to 2030, we took into account the substitution and complementarity between crops, as well as the correlation between sales volume, sales price, and planting cost, and introduced a new random variable - the Spearman correlation coefficient. By calculating the Spilman correlation coefficient for each crop, we obtained a strong correlation between expected sales volume and planting cost. Then, we used K-means clustering analysis to classify crops, resulting in three different categories that achieve complementarity within each class and substitution between classes. This article included the correlation coefficient and adjustment parameters in the objective function and obtained the optimal crop planting scheme through Monte Carlo simulation.