Multi-stage Crop Planting Strategy optimization Model Based on PSO Algorithm

This paper presents a multi-stage crop planting strategy optimization model utilizing the Particle Swarm Optimization (PSO) algorithm. The model aims to address key challenges in agriculture, such as fluctuating market demands, climate variability, and rising production costs. Over a seven-year planning horizon (2024-2030), the model dynamically adjusts crop planting strategies by integrating factors like crop yield, market prices, and crop rotation. The proposed framework optimizes both crop selection and area allocation to maximize economic profitability while maintaining sustainable practices. Unlike traditional static models, this approach accounts for uncertainties and adapts to real-time changes in market conditions. The model's ability to adjust planting strategies based on PSO ensures that farmers can optimize yields and reduce waste through flexible decision-making. Experimental results demonstrate the model's effectiveness in dynamically optimizing planting strategies while satisfying constraints such as land availability and crop rotation.