Genetic Algorithm Based Optimal Strategy for Crop Planting Plots and Selections

Yangyuan Lai; Xibo Lin; Jingbo Yang; Hong Wang

doi:10.2991/978-94-6463-746-5_10

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Genetic Algorithm Based Optimal Strategy for Crop Planting Plots and Selections

Authors

Yangyuan Lai¹, Xibo Lin¹, Jingbo Yang¹, Hong Wang²^{, *}

¹College of Engineering Physics, Shenzhen Technology University, Shenzhen, 518118, China

²College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China

^*Corresponding author. Email: hitwanghong@163.com

Corresponding Author

Hong Wang

Available Online 27 May 2025.

DOI: 10.2991/978-94-6463-746-5_10 How to use a DOI?
Keywords: Planting Strategy; Genetic Algorithm; Linear Programming; Spearman Rank Correlation Coefficient
Abstract: This study proposes a crop planting optimisation strategy based on a genetic algorithm that integrates agricultural production principles and market factors to provide a scientifically sound and rational approach to maximising profitability. Firstly, a linear programming model is constructed for the purpose of analysing common crops and market dynamics, thereby facilitating the scientific allocation of agricultural resources. Subsequently, in order to account for the volatility of key factors, such as market demand and crop yields, Spearman’s correlation coefficient is employed for the purpose of analysing variable relationships. The introduction of complementarity and substitutability parameters enables the simulation of dynamic changes in dominant factors among crops, thereby generalising the model to adapt to different scenarios. The experimental results demonstrate that the proposed strategy exhibits stability, realism, and generalisability, thereby providing effective support for rural crop planting planning.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2025 International Conference on Agriculture and Resource Economy (ICARE 2025)
Series: Advances in Biological Sciences Research
Publication Date: 27 May 2025
ISBN: 978-94-6463-746-5
ISSN: 2468-5747
DOI: 10.2991/978-94-6463-746-5_10 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yangyuan Lai
AU  - Xibo Lin
AU  - Jingbo Yang
AU  - Hong Wang
PY  - 2025
DA  - 2025/05/27
TI  - Genetic Algorithm Based Optimal Strategy for Crop Planting Plots and Selections
BT  - Proceedings of the 2025 International Conference on Agriculture and Resource Economy (ICARE 2025)
PB  - Atlantis Press
SP  - 85
EP  - 96
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-746-5_10
DO  - 10.2991/978-94-6463-746-5_10
ID  - Lai2025
ER  -

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