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Research on Crop Planning Based on Data Mining and Genetic Algorithms

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DOI: 10.23977/jeis.2024.090319 | Downloads: 10 | Views: 187

Author(s)

Shaosong Zhang 1, Yuhang Chen 1, Liping Du 1

Affiliation(s)

1 College of Science, North China University of Science and Technology, Tangshan, China

Corresponding Author

Liping Du

ABSTRACT

Data mining techniques can be employed to extract information that is not immediately apparent from large amounts of data, and to construct predictive models based on this extracted information. These models can then be used as a basis for decision-making. In order to expand the scope of its application, this paper combines data mining with genetic algorithms and orthogonal experiments and applies it to the optimization of planting decisions. In particular, this study initially gathered and structured data on planting conditions, crop sales, per-mu yields, planting costs, and selling prices in a village through data mining techniques and subsequently analyzed the intrinsic relationships between these variables. On this basis, this paper constructs a planning function with the goal of maximizing profits and uses genetic algorithms to solve optimization problems. Overall, this study has successfully applied data mining techniques to practical planting decision-making problems, which not only has strong practicality, but also provides a reference for solving other complex planning problems. In the future, further exploration of the integration of additional optimization algorithms into the data-driven decision-making analysis framework may yield more comprehensive solutions.

KEYWORDS

Data mining, linear programming, orthogonal experiment, genetic algorithm

CITE THIS PAPER

Shaosong Zhang, Yuhang Chen, Liping Du, Research on Crop Planning Based on Data Mining and Genetic Algorithms. Journal of Electronics and Information Science (2024) Vol. 9: 142-152. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090319.

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