TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Genetic algorithm-based optimization for enhancing crop yield and resource efficiency in agriculture

*

* Corresponding author · click or hover a name for details

pp. 1059–1067Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1891XML
Received:
16 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1891
Pages:
1059–1067

Abstract

Maximization of crop yield with minimization of resource inputs is one of major problem faced by different agricultural practices. Global food security depends on achieving sustainable crop yield and resource efficiency in agriculture. Promising approach to handle these optimization problems is offered by Genetic Algorithms that is based on iteratively evolving solutions based on natural selection and genetic inheritance principles. This paper presents the application of Genetic Algorithm in optimizations of agricultural practices, with a special focus on increasing crop yield and resource efficiency. The findings of this paper highlight essential role Genetic Algorithms plays in promoting sustainable practices, revolutionizing agricultural decision-making, and granting robustness in the face of evolving environmental challenges and economic pressures.

Keywords

Subject Classifications

Primary 68W40Secondary 68W50

References

[1] K. Pawlak and M. Kołodziejczak, “The Role of Agriculture in Ensuring Food Security in Developing Countries: Considerations in the Context of the Problem of Sustainable Food Production,” Sustainability, vol. 12, no. 13, Art. no. 13 (Jan. 2020), doi: 10.3390/su12135488.
[2] P. Sahu and C. Debsarma, “Climate Change and Urban Environment Sustainability: Issues and Challenges,” in Climate Change and Urban Environment Sustainability, B. Pathak and R. S. Dubey, Eds., Singapore: Springer Nature, pp. 1–13 (2023). doi: 10.1007/978-981-19-7618-6_1.
[3] S. H. Muhie, “Novel approaches and practices to sustainable agriculture,” J. Agric. Food Res., vol. 10, p. 100446 (Dec. 2022), doi: 10.1016/j.jafr.2022.100446.
[4] G. Dutta, S. Paul, S. Dey, and H. Pathak, “Climate Change Impacts and Adaptation Strategies for Agronomic Crops,” in Climate Change Impacts on Soil-Plant-Atmosphere Continuum, H. Pathak, D. Chatterjee, S. Saha, and B. Das, Eds., Singapore: Springer Nature, pp. 383–404 (2024). doi: 10.1007/978-981-99-7935-6_14.
[5] B. Alhijawi and A. Awajan, “Genetic algorithms: theory, genetic operators, solutions, and applications,” Evol. Intell., vol. 17, no. 3, pp. 1245–1256 (Jun. 2024), doi: 10.1007/s12065-023-00822-6.
[6] S. M. Elsayed, R. A. Sarker, and D. L. Essam, “A new genetic algorithm for solving optimization problems,” Eng. Appl. Artif. Intell., vol. 27, pp. 57–69 (Jan. 2014), doi: 10.1016/j.engappai.2013.09.013.
[7] J. L. J. Pereira, G. A. Oliver, M. B. Francisco, S. S. Cunha, and G. F. Gomes, “A Review of Multi-objective Optimization: Methods and Algorithms in Mechanical Engineering Problems,” Arch. Comput. Methods Eng., vol. 29, no. 4, pp. 2285–2308 (Jun. 2022), doi: 10.1007/s11831-021-09663-x.
[8] L. Bi and G. Hu, “A genetic algorithm-assisted deep learning approach for crop yield prediction,” Soft Comput., vol. 25, no. 16, pp. 10617–10628 (Aug. 2021), doi: 10.1007/s00500-021-05995-9.
[9] Y. Guo, “Integrating genetic algorithm with ARIMA and reinforced random forest models to improve agriculture economy and yield forecasting,” Soft Comput., vol. 28 (Dec. 2023), doi: 10.1007/s00500-023-09516-8.
[10] A. Peerlinck, J. Sheppard, J. Pastorino, and B. Maxwell, “Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm,” in 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand: IEEE, pp. 1838–1845 (Jun. 2019). doi: 10.1109/CEC.2019.8790267.
[11] H. Lingaraj, “A Study on Genetic Algorithm and its Applications,” Int. J. Comput. Sci. Eng., vol. 4, pp. 139–143 (Oct. 2016).
[12] “A novel Interactive Approach for Solving Uncertain Bi-Level Multi-Objective Supply Chain Model | Request PDF.” Accessed: Jun. 26 (2024). [Online]. Available: https://www.researchgate.net/publication/360458825_A_novel_Interactive_Approach_for_Solving_Uncertain_Bi-Level_Multi-Objective_Supply_Chain_Model 
[13] N. Sortrakul, H. L. Nachtmann, and C. R. Cassady, “Genetic algorithms for integrated preventive maintenance planning and production scheduling for a single machine,” Comput. Ind., vol. 56, no. 2, pp. 161–168 (Feb. 2005), doi: 10.1016/j.compind.2004.06.005. 
[14] J. Liu and L. Tang, “A modified genetic algorithm for single machine scheduling,” Comput. Ind. Eng., vol. 37, no. 1, pp. 43–46 (Oct. 1999), doi: 10.1016/S0360-8352(99)00020-0.
[15] M. Amin, A. Ghaly, F. Ayad, and O. Hosny, “Overall Schedule optimization using genetic algorithms,” in Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022, R. Gupta, M. Sun, S. Brzev, M. S. Alam, K. T. W. Ng, J. Li, A. El Damatty, and C. Lim, Eds., Cham: Springer Nature Switzerland, pp. 449–461 (2024). doi: 10.1007/978-3-031-35471-7_33.
[16] E. Elbeltagi, M. Ammar, H. Sanad, and M. Kassab, “Overall multiobjective optimization of construction projects scheduling using particle swarm,” Eng. Constr. Archit. Manag., vol. 23, no. 3, pp. 265–282 (Jan. 2016), doi: 10.1108/ECAM-11-2014-0135.
[17] M. H. Nili, H. Taghaddos, and B. Zahraie, “Integrating discrete event simulation and genetic algorithm optimization for bridge maintenance planning,” Autom. Constr., vol. 122, p. 103513 (Feb. 2021), doi: 10.1016/j.autcon.2020.103513.
[18] R. González Perea, E. Camacho Poyato, P. Montesinos, and J. A. Rodríguez Díaz, “Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms,” Water Resour. Manag., vol. 30, no. 8, pp. 2815–2830 (Jun. 2016), doi: 10.1007/s11269-016-1325-7.
[19] M. Moradi-Jalal, S. I. Rodin, and M. A. Mariño, “Use of Genetic Algorithm in Optimization of Irrigation Pumping Stations,” J. Irrig. Drain. Eng., vol. 130, no. 5, pp. 357–365 (Oct. 2004), doi: 10.1061/(ASCE)0733-9437(2004)130:5(357).
[20] S. Gholizadeh-Tayyar, U. Okongwu, and J. Lamothe, “A Heuristic-Based Genetic Algorithm for Scheduling of Multiple Projects Subjected to Resource Constraints and Environmental Responsibility Commitments,” Process Integr. Optim. Sustain., vol. 5, no. 3, pp. 361–382 (Sep. 2021), doi: 10.1007/s41660-020-00150-7.
[21] “AgricultureRecommendation 99% acc.” Accessed: Jun. 26 (2024). [Online]. Available: https://www.kaggle.com/code/casper6290/agriculturerecommendation-99-acc/input.

Views: 95Downloads: 20Citations: 0