Research on crop planting strategy based on whale optimization algorithm

Leru Sun; Jianyuan Xu

doi:10.54097/t9exw737

Authors

Leru Sun
Jianyuan Xu

DOI:

https://doi.org/10.54097/t9exw737

Keywords:

Genetic Algorithm, Whale Optimization, Algorithm, Pearson's coefficient.

Abstract

In this paper, the problem of farming strategy of arable land is studied in depth, and the planning model of the optimal planting scheme for decision-making crops. First, multi-objective hybrid planning is constructed by adding constraints. Secondly, the optimal planting strategy that satisfies the multi-objective conditions is found by imitating the law of whale predation and optimized using genetic algorithm; The results showed that the model could significantly improve the planting yield compared with the previous planting structure. Finally, based on this model, the crop planting model of whale optimization algorithm is constructed by considering the uncertainty factor. The results showed that the overall results were more stable after continuous iteration of the offspring, considering the various risks that will be faced in the process of agricultural planting. The research in this paper has significantly increased crop yields and provided a relatively effective planting method, which offers valuable insights for real-world farming strategies.

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References

[1] Tester M, Langridge P. Breeding technologies to increase crop production in a changing world[J]. Science, 2010, 327(5967):

[2] Koul B, Yakoob M, Shah M P. Agricultural waste management strategies for environmental sustainability[J]. Environmental Research, 2022, 206: 112285.

[3] Mirzaei A, Zibaei M. Water conflict management between agriculture and wetland under climate change: Application of economic-hydrological-behavioral modelling[J]. Water Resources Management, 2021, 35(1): 1-21.

[4] Alibabaei K, Gaspar P D, Lima T M, et al. A review of the challenges of using deep learning algorithms to support decision-making in agricultural activities[J]. Remote Sensing, 2022, 14(3): 638.

[5] Krishna V, Reddy T, Harsha S, et al. Analysis of crop yield prediction using machine learning algorithms[C]//2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT). IEEE, 2022: 1-4.

[6] Maraveas C, Asteris P G, Arvanitis K G, et al. Application of bio and nature-inspired algorithms in agricultural engineering[J]. Archives of Computational Methods in Engineering, 2023, 30(3): 1979-2012.

[7] Juraev F D, Mallaev A R, Aralov G M, et al. Algorithms for improving the process of modeling complex systems based on big data: On the example of regional agricultural production[C]//E3S Web of Conferences. EDP Sciences, 2023, 392: 01050.

[8] Wu Y, Li X, Liu Q, et al. The analysis of credit risks in agricultural supply chain finance assessment model based on genetic algorithm and backpropagation neural network[J]. Computational Economics, 2021: 1-24.

[9] Rana P, Varshney L R. Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusion[J]. arxiv preprint arxiv:2009.08002, 2020.

[10] Rajasekaran S, Balla S, Huang C H. Exact algorithms for planted motif problems[J]. Journal of Computational Biology, 2005, 12(8).