Optimal Crop Planting Scheme Based on Linear Programming Model and Particle Swarm Optimization

With population growth and climate change, crop planting optimization has become increasingly critical in modern agriculture. The core challenge lies in maximizing economic benefits while satisfying planting constraints. This paper utilizes actual planting data from a rural area in North China Mountains to propose an optimized solution for enhancing land resource utilization and achieving sustainable rural economic development. By integrating particle swarm optimization (PSO) with a linear programming model, this paper establish a multi-constrained optimization framework focusing on annual, plot-specific, and seasonal planting area allocation. The objective function prioritizes total revenue maximization under considerations such as plot type restrictions, continuous cropping avoidance, and legume planting frequency constraints. Results demonstrate that the optimized scheme achieves a 15% and 12% increase in total revenue under two distinct sales strategies (2024–2030), respectively. This research not only provides actionable insights for agricultural optimization in North China but also serves as a reference for similar regions globally.