Research on crop planting strategy based on improved genetic algorithm

Wenbo Zhang; Jingzhao Feng; Caizhi Tian

doi:10.54097/vfrpyx90

Authors

Wenbo Zhang
Jingzhao Feng
Caizhi Tian

DOI:

https://doi.org/10.54097/vfrpyx90

Keywords:

Planting Strategies, Optimisation Algorithms, Genetic Algorithms, Statistical Learning.

Abstract

With the continuous development of China's national economy and the steady promotion of the rural revitalization strategy, optimizing crop planting strategies has become increasingly prominent. In this study, a crop planting strategy optimization method based on an improved genetic algorithm is proposed based on careful consideration of planting area, crop rotation system, crop growing season, and topography. Specifically, on the one hand, this method uses hyperbolic tangent function fitting, Pearson correlation coefficient, and hierarchical analysis to construct a correlation coefficient model, and specifies alternative and complementary constraints. On the other hand, the relationship between expected sales volume and sales price, and planting cost is analyzed by Lasso regression, the corresponding objective function is designed, and the proposed optimization scheme is solved by the improved genetic algorithm. The results of simulation experiments verified the effectiveness of the method and provided strong support for achieving scientific and rational agricultural cultivation. This paper innovatively proposed an improved genetic algorithm to optimize the planting strategy, effectively optimize the planting strategy, and improve the economic benefits of crops.

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