Research on Planting Strategies Based on K-Means Cluster Analysis and Spearman Correlation Analysis

K-Means clustering is a commonly used iterative clustering algorithm in machine learning that categorizes many samples into clusters with the same attributes based on similarities between the samples. At the same time, Spearman correlation analysis is used to characterize possible correlations between different factors. To deepen the combined application of these two methods, this paper combines K-Means cluster analysis and Spearman correlation analysis and applies them to the optimization of planting strategies. Firstly, this paper considers various practical constraints in the planting process and formulates a planning model with the objective of maximizing the total return. Then, through K-Means clustering analysis, crops are divided into clusters with different traits, and Spearman correlation analysis is used to introduce the substitution and complementary relationship between different clusters, so as to construct a planting strategy planning model that better meets the market demand. The model not only has strong practicality, but also provides scientific planting strategies for decision makers. In addition, this paper adopts a combination of multiple machine learning algorithms, which provides a valuable reference for the integration and application of the model. Through this research, we provide new perspectives and tools for agricultural decision-making and promote scientific and rational planting strategies.