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Performance modeling and optimization of a smart sensor-based Hydroponic system using PSO and Genetic algorithms
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, 412115, India. amit303singh@gmail.com.
Scientific Reports
|June 23, 2026
Summary
Smart farming relies on dependable automated hydroponic systems. Stochastic modeling and optimization algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) significantly enhance system reliability and availability for sustainable agriculture.
Area of Science:
- Agricultural Engineering
- Systems Engineering
- Reliability Engineering
Background:
- Sustainable agriculture increasingly depends on automated hydroponic systems for stable food production, especially in resource-limited areas.
- Ensuring the continuous operation of smart farming technology, including sensors, microcontrollers, and water pumps, is crucial.
Purpose of the Study:
- To improve the reliability and availability of automated hydroponic systems.
- To explore the application of stochastic modeling and nature-inspired optimization algorithms for system performance enhancement.
Main Methods:
- A Continuous-Time Markov Chain (CTMC) framework was used for stochastic modeling of system states (operational, degraded, failed).
- Chapman-Kolmogorov differential equations were solved using Laplace transformation and the supplementary variable technique.
- Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were employed to optimize system parameters and minimize downtime.
Main Results:
- The CTMC model provided time-dependent probabilities to estimate system reliability and availability.
- PSO achieved a maximum availability of 0.9737, while GA reached a maximum availability of 0.9753.
- Both algorithms demonstrated effectiveness in parameter tuning for maintenance planning.
Conclusions:
- Stochastic modeling combined with optimization techniques (PSO, GA) offers a robust approach to enhance hydroponic system reliability and availability.
- This methodology aids in predictive maintenance and understanding long-term system behavior for sustainable smart farming.
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