Research on Optimal Crop Planting Problem Based on Fuzzy Planning Modeling

Abstract

With the increasing importance of rural economic development, the optimization of crop planting strategies on limited arable land has become crucial. This study utilizes fuzzy planning models and particle swarm optimization (PSO) algorithms to improve production efficiency and reduce cultivation risks. The fuzzy model tackles the uncertainties in crop prices and sales through affinity functions, while the PSO algorithm searches iteratively for optimal solutions under constraints such as soil degradation and crop rotation. The case study results reveal clear trends: under Scenario 1 (2024–2030), crops 1 and 2 maintained high planting areas due to stable market demand, while crops 3 and 4 showed significant fluctuations. In Scenario 2, there was a sharp increase in the acreage of crop 5 after 2028, reflecting the model's adaptive response to market dynamics. These findings demonstrate the model's capability to optimize resource allocation under uncertainty, providing scalable solutions for rural agricultural planning. By combining fuzzy logic with swarm intelligence, this research establishes a robust framework for stabilizing farmers' incomes and promoting long-term agricultural resilience. The integration of these advanced techniques not only enhances economic returns but also ensures sustainable land use, aligning with environmental considerations and supporting the sustainable development of agriculture.