Research on optimal crop planting strategy based on particle swarm algorithm and Monte Carlo model

Bowen Tan; Song Xue

doi:10.54097/h03ax129

Authors

Bowen Tan
Song Xue

DOI:

https://doi.org/10.54097/h03ax129

Keywords:

Particle Swarm Optimization, Monte Carlo Simulation, Planning Model.

Abstract

This paper establishes a crop planting model based on particle swarm algorithm and Monte Carlo simulation to address rural planting problems in the mountainous areas of North China, and obtains the optimal planting plan with the goal of maximizing returns. First, through data preprocessing and analysis, it was found that the 2023 planting plan has crop planting dispersion and yields affected by plots, seasonal and other problems. The planning analysis model is used and the particle swarm algorithm is used to predict the best planting strategy under the constraints of different conditions. Secondly, further increase passive factors and risk factors, and establish a Monte Carlo model to predict fluctuations. Taking into account the other various influencing factors, the Monte Carlo model is used to quantify the influencing factors, and the particle swarm algorithm is used to obtain the best planting strategy. Finally, by establishing a fit model, the correlation between expected sales volume, sales price and planting cost is analyzed, and it is used as a constraint to further optimize the planting plan to maximize profits.

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