Biological optimization of sustainable agricultural systems through genetic algorithms and nitrogen balance management
Abstract
In order to improve resource efficiency and enhance sustainability in biological systems, this study investigates the optimization of biomechanical processes by combining genetic algorithms (GA) with human performance and recovery management. The study aims to minimize injury risks and maximize recovery efficiency by utilizing GA to model biomechanical processes. To ensure a dynamic balance in physical performance, the study presents an ideal optimization framework in which human biomechanics is optimized for enhanced sports performance and injury prevention. The model considers factors such as muscle strain, joint impact, and fatigue recovery to create a holistic biomechanical optimization system. By integrating principles from biological nutrient cycles—such as the efficient use of resources and minimizing waste—the approach highlights the parallels between sustainable agricultural systems and sustainable biomechanics. This framework ensures that optimization strategies not only improve performance outcomes but also maintain long-term musculoskeletal health. The research demonstrates how combining biological insights with advanced computational methods can address both physical health and performance challenges in biomechanics. Through multi-objective optimization, the work offers a novel perspective on integrating biological processes with biomechanics to support sustainable human activity and recovery, contributing to advancements in sports science, rehabilitation, and human physical performance.
References
1. Sakthipriya S, Naresh R. Precision agriculture based on convolutional neural network in rice production nutrient management using machine learning genetic algorithm. Engineering Applications of Artificial Intelligence. 2024; 130: 107682. doi: 10.1016/j.engappai.2023.107682
2. Zhu F, Zhang L, Hu X, et al. Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops. Agronomy. 2023; 13(5): 1423. doi: 10.3390/agronomy13051423
3. Tynchenko V, Kukartseva O, Tynchenko Y, et al. Predicting Tilapia Productivity in Geothermal Ponds: A Genetic Algorithm Approach for Sustainable Aquaculture Practices. Sustainability. 2024; 16(21): 9276. doi: 10.3390/su16219276
4. Zhan Y, Zhu J. Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion. Applied Energy. 2024; 355: 122336. doi: 10.1016/j.apenergy.2023.122336
5. Masuda K. Combined Application of a Multi-Objective Genetic Algorithm and Life Cycle Assessment for Evaluating Environmentally Friendly Farming Practices in Japanese Rice Farms. Sustainability. 2023; 15(13): 10059. doi: 10.3390/su151310059
6. Dogan H, Aydın Temel F, Cagcag Yolcu O, et al. Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm. Bioresource Technology. 2023; 370: 128541. doi: 10.1016/j.biortech.2022.128541
7. Fernández Izquierdo P, Cerón Delagado L, Ortiz Benavides F. An artificial neuronal network coupled with a genetic algorithm to optimise the production of unsaturated fatty acids in Parachlorella kessleri. Artificial Intelligence in Agriculture. 2024; 13: 32-44. doi: 10.1016/j.aiia.2024.06.003
8. Cui C. Full-size Computer Simulation Model Design of 3D Braided Composites. Mari Papel Y Corrugado. 2024; 2024(1): 98–105.
9. Hamouda YEM. Optimally sensors nodes selection for adaptive heterogeneous precision agriculture using wireless sensor networks based on genetic algorithm and extended Kalman filter. Physical Communication. 2024; 63: 102290. doi: 10.1016/j.phycom.2024.102290
10. Mohamad N, Ab. Aziz NA, Ghazali AK, Salleh MR. Improving Ammonia Emission Model of Urea Fertilizer Fluidized Bed Granulation System Using Particle Swarm Optimization for Sustainable Fertilizer Manufacturing Practice. Processes. 2024; 12(5): 1025. doi: 10.3390/pr12051025
11. Kalichkin VK, Maksimovich KYu, Fedorov DS, et al. Conceptual Model of Digital Nitrogen Management in Agricultural Crops. Russian Agricultural Sciences. 2024; 50(2): 197-206. doi: 10.3103/s1068367424700071
12. Sun Y, Zhang J, Bai J, et al. Comprehensive assessment of soil quality in greenhouse agriculture based on genetic algorithm and neural network. Journal of Soils and Sediments. 2023; 24(3): 1302-1315. doi: 10.1007/s11368-023-03706-5
13. Feng T, Liu B, Ren H, et al. Optimized model for coordinated development of regional sustainable agriculture based on water-energy-land-carbon nexus system: A case study of Sichuan Province. Energy Conversion and Management. 2023; 291: 117261. doi: 10.1016/j.enconman.2023.117261
14. Kontos YN, Rompis I, Karpouzos D. Optimal Pollution Control and Pump-and-Fertilize Strategies in a Nitro-Polluted Aquifer, Using Genetic Algorithms and Modflow. Agronomy. 2023; 13(6): 1534. doi: 10.3390/agronomy13061534
15. Wang X, Liu J, Zhang C. Network intrusion detection based on multi-domain data and ensemble-bidirectional LSTM. EURASIP Journal on Information Security. 2023; 2023(1). doi: 10.1186/s13635-023-00139-y
16. Vázquez-Sánchez AY, Lima EC, Abatal M, et al. Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm. Molecules. 2023; 28(17): 6387. doi: 10.3390/molecules28176387
17. Zahmatkesh S, Gholian-Jouybari F, Klemeš JJ, et al. Sustainable and optimized values for municipal wastewater: The removal of biological oxygen demand and chemical oxygen demand by various levels of geranular activated carbon- and genetic algorithm-based simulation. Journal of Cleaner Production. 2023; 417: 137932. doi: 10.1016/j.jclepro.2023.137932
18. Mathur P, Singh S. Advanced Anaerobic Digestion With Optimization Techniques Using Genetic Algorithm and Fuzzy Logic. Indian Journal of Science And Technology. 2023; 16(22): 1624-1634. doi: 10.17485/ijst/v16i22.2195
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
