Optimization of Crop Planting Strategies Based on GA-AM Algorithm and Monte Carlo Simulation

In this paper, a hybrid multi-period planning model combining GA-AM algorithm and Monte Carlo simulation is proposed for optimizing crop planting strategies. The study first constructs a decision model containing various key economic indicators through data analysis. Based on this, the model was solved by GA-AM algorithm to derive the optimal planting scheme by combining the resources and crop planting strategies in different scenarios. In addition, for the uncertainties in crop cultivation, this paper further introduces the Monte Carlo simulation algorithm to consider the potential risks and optimize the decision. Finally, the K-means algorithm is used to introduce new crop nature constraints by analyzing crop substitutability and complementarity, which enhances the model's adaptability to market fluctuations. The study shows that the model not only has strong practical application value, but also provides effective theoretical support and practical guidance for agricultural supply chain optimization.