Study on Crop Planting Planning Based on Goal Programming and Particle Swarm Optimization

Abstract

Faced with the challenges posed by global population growth and climate change, enhancing agricultural production efficiency and resource utilization, as well as scientifically planning crop planting schemes to adapt to various field characteristics and market demands, has become a core issue in modern agriculture. In actual production, numerous constraints complicate the planning of crop planting schemes. To address this issue, this study establishes a goal programming model for production planning, predicting the annual profit and planting scheme over the next seven years in two scenarios and solving it with the Particle Swarm Optimization (PSO) method. On this basis, a dynamic adjustment of the inertia weight method was introduced to accelerate convergence. Results show that, with this model, the average annual profits for the two scenarios reached 5,385,399 and 6,145,969 yuan, representing increases of 7.70% and 22.91% compared to 2023. The convergence speed with dynamic inertia weight adjustment was 2.92 and 2.09 times faster than without .This study provides a practical tool for enhancing agricultural efficiency, boosting economic returns, and supporting smart agriculture while addressing global food security and sustainability challenges.