A village in the mountainous region of North China faces the challenges of limited arable land resources and low-temperature environments, and needs to optimise crop planting strategies to enhance production efficiency and reduce risks. To this end, this paper proposes an optimal crop planting strategy model that integrates linear programming, Monte Carlo simulation and particle swarm algorithm. Firstly, the optimal planting strategy was formulated by linear programming and particle swarm algorithm without considering the uncertainty; secondly, the uncertainty of the expected future sales volume, mu yield, planting cost and sales price was further considered, and a dynamic planning model was established by Monte Carlo simulation and particle swarm algorithm, which yielded the average profit of the calendar year to be increased to 62,346,800 Yuan; finally, inter-crop substitutability was introduced to explore the correlation between crops using K-means cluster analysis and a linear regression model of expected sales was developed by least squares. The results showed that the planting strategy after considering inter-crop correlation could further improve the production efficiency. The study in this paper is of great value for future extension and can be applied to areas with similar agro-ecological environments and cropping conditions to provide guidance for local agricultural cropping strategies.

Qianyun Zhou, Huyi Li, Shihan Yang. Research on optimal crop planting strategy based on particle swarm algorithm. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 2: 90-98. https://doi.org/10.25236/AJCIS.2025.080212.

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