Author(s)

YingWei Xie, WenYue Wang, TianYu Lan^*

Affiliation(s)

School of Big Date, Fuzhou University of International Studies and Trade, Fuzhou 350202, Fujian, China.

Corresponding Author

TianYu Lan

ABSTRACT

With the expansion of agricultural production scale and diversification of market demands, scientific and rational crop planting planning is of great significance for improving agricultural production efficiency. This study aims to optimize crop planting plans using genetic algorithms to solve this complex multi-dimensional decision problem. The research establishes an optimization model with profit maximization as the objective, considering multiple constraints including land type restrictions, crop rotation requirements, and crop distribution. Two sales scenarios were designed: unsalable when exceeding expected sales volume (Scenario 1) and selling at half price (Scenario 2). Through an improved genetic algorithm utilizing multi-matrix chromosome coding, the study effectively handles multi-dimensional decision variables involving plots, years, seasons, and crops. Results show that Scenario 2 yields significantly higher profits (1.4×107 yuan) compared to Scenario 1 (2.9×106 yuan). In terms of crop yield distribution, cowpea, sword bean, kidney bean, potato, and tomato rank as the top five; regarding cultivated land area distribution, dry land shows the highest utilization rate, indicating its superior economic benefits. This study provides a practical decision-support tool for agricultural production planning.

KEYWORDS

Crop planting plan; Genetic algorithm; Multi-Matrix chromosome coding; Profit maximization

CITE THIS PAPER

YingWei Xie, WenYue Wang, TianYu Lan. Optimizing crop planting plans based on genetic algorithms. Journal of Computer Science and Electrical Engineering. 2025, 7(1): 27-32. DOI: https://doi.org/10.61784/jcsee3034.

REFERENCES

[1] MAJA M M, AYANO S F. The impact of population growth on natural resources and farmers’ capacity to adapt to climate change in low-income countries. Earth Systems and Environment, 2021, 5(2): 271-283.

[2] WANG X. Managing land carrying capacity: Key to achieving sustainable production systems for food security. Land, 2022, 11(4): 484.

[3] ZHOU Y, ZHANG E, LIN A. Evaluation of comprehensive use benefits and analysis of influencing factors of China’s natural resources: based on a new spatial model. Environment, Development and Sustainability, 2024, 1-39.

[4] JI W, LIU D, MENG Y, et al. A review of genetic-based evolutionary algorithms in SVM parameters optimization. Evolutionary Intelligence, 2021, 14: 1389-1414.

[5] VIE A, KLEINNIJENHUIS A M, FARMER D J. Qualities, challenges and future of genetic algorithms: a literature review. arXiv preprint arXiv:2011.05277, 2020.

[6] RAJWAR K, DEEP K, DAS S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 2023, 56(11): 13187-13257.

[7] KARAMIAN F, MIRAKZADEH A A, AZARI A. Application of multi-objective genetic algorithm for optimal combination of resources to achieve sustainable agriculture based on the water-energy-food nexus framework. Science of The Total Environment, 2023, 860: 160419.

[8] ALAM T, QAMAR S, DIXIT A, et al. Genetic algorithm: Reviews, implementations, and applications. arXiv preprint arXiv:2007.12673, 2020.

[9] TAHA Z Y, ABDULLAH A A, RASHID T A. Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications. arXiv preprint arXiv:2409.14563, 2024.

[10] ZHANG D, YANG S, LI S, et al. Integrated Optimization of the Location–Inventory Problem of Maintenance Component Distribution for High-Speed Railway Operations. Sustainability, 2020, 12(13): 5447.