Author(s)

TianYou Zuo^1*, LiangYing Ji², YuSa Liu³

Affiliation(s)

¹Department of School of Overseas Education, Changzhou University, Changzhou 213164, Jiangsu, China.

²Department of School of Safety and Engineering, Changzhou University, Changzhou 213164, Jiangsu, China.

³Department of College of pharmacy, Changzhou University, Changzhou 213164, Jiangsu, China.

Corresponding Author

TianYou Zuo

ABSTRACT

With the development of rural economy, optimizing planting strategy is an important research topic in organic planting industry. At present, most experts are not clear about the binding conditions for crop planting. This paper searched for data on agricultural products and local soil conditions in Hutang Town, Changzhou City, Jiangsu Province, and identified data on local cropping strategies for 2023. Based on the above data and the ideas of linear programming and multi-objective programming, the planting strategy optimization model under multiple constraints was established by determining planting risk factors and other indicators to seek the optimal crop planting strategy with maximum returns, and particle swarm optimization algorithm was used to solve the model. Finally, the optimal planting strategy under certain conditions and uncertain factors is obtained, and the complex problems arising from the optimal crop planting strategy are solved.

KEYWORDS

Linear programming; Monte carlo simulation; Poisson process; Particle swarm optimization

CITE THIS PAPER

TianYou Zuo, LiangYing Ji, YuSa Liu. Optimal crop planting strategy based on particle swarm algorithm. Eurasia Journal of Science and Technology. 2025, 7(5): 17-20. DOI: https://doi.org/10.61784/ejst3109.

REFERENCES

[1] Xu Zhihong, Sun Caixin, Zhang Xiaoxing, et al. The Development Status and Future Trends of Chinese Agriculture. Transactions of China Electrotechnical Society, 2021, 19(10): 53-58.

[2] Fangfang. Analysis of Crop Cultivation Techniques and Factors Affecting High Yield. Harbin Institute of Technology, 2022, 34(2): 57-63.

[3] Ma Xuesong. Comprehensive data: area under cultivation of major crops and trends in China. IEEE Transactions on Power Systems, 2021, 16(4): 798-805.

[4] Ma Kunlong. Monte Carlo and Quasi-Monte Carlo Methods. Changsha: Hunan University, 2020, 6(1):107-115.

[5] Shen Xianjun. Adaptive particle swarm optimisation algorithm and its applications. Systems Engineering-Theory and Practice, 2022, 30(1): 158-160.

[6] Tang Guosheng. Particle swarm and applications. Journal of Higher Education, 2023, 9(11): 53-56.

[7] Liu Zhimei. Deep integration of Mathematical Modeling and Mathematics Teaching in Higher Vocational Colleges. Journal of Jiamusi Vocational College,2023, 39(03):152-154.

[8] Xu Yatao, Wu Libao. Research on Evaluation index system of Mathematical modeling teaching in Senior high school based on Delphi AHP. Journal of neijiang normal college, 2023, 38(02): 113-119.

[9] Yang Benchao, Shi Yaan, Duan Ganheng, et al. Analysis of price fluctuation of agricultural products in China and its influencing factors. University Education, 2023(04): 44-46.

[10] Huang Jian, Xu Binyan. Research on crop production risk management based on Monte Carlo simulation. Journal of Mathematics Education, 2020, 32(01): 93-98.