Optimal Crop Planting Based on the Monte Carlo-Simulated Annealing Algorithm

Abstract

This article elucidates the critical importance of sustainable land resource management in safeguarding human well-being amid globalization and urbanization, with a particular emphasis on the optimal utilization of cultivated land resources in the mountainous regions of North China. The study focuses on the cultivated land resources within a specific village in these areas, encompassing open-air cropland, conventional greenhouses, and smart greenhouses. Data is sourced from actual plot classifications and crop cultivation conditions. A Markov chain model is employed to predict planting plot types, integrated with linear programming techniques and Monte Carlo simulated annealing algorithms to maximize total revenue while minimizing unsold waste. The findings demonstrate that a multi-objective optimization model incorporating competitive and cooperative game theory can effectively propose scientifically grounded planting structures that enhance both economic returns and environmental sustainability. This model offers valuable insights for the conservation and utilization of cultivated land resources not only in North China's mountainous regions but also globally, thereby advancing agricultural practices towards greater efficiency, ecological protection, and sustainability.