What Are the Optimal Sampling Time of Environmental Parameters? Fourier Analysis and Energy Harvesting to Reduce Sensors Consumption in Smart Greenhouses

Smart greenhouses offer crucial solutions for reducing atmospheric impact and resource waste. However, two fundamental challenges persist in their implementation: massive energy consumption and a high level of human intervention, particularly for sensor battery replacement or recharging. Unfortunately, sensors are indispensable in greenhouses and agriculture, such as for monitoring environmental parameters for air quality assessment. Therefore, while sensors cannot be eliminated, it is essential to optimize their energy consumption. This work introduces an energy-efficient monitoring system for smart greenhouses, aiming to reduce the energy consumption of individual sensors and enhance system sustainability. The study focuses on optimizing the sampling intervals of commonly monitored environmental parameters to minimize sensor energy usage while maintaining data acquisition accuracy adequate for the intended purpose. Additionally, to further reduce battery energy draw, an energy harvesting system using solar panels has been implemented. In conclusion, adopting an optimal sampling strategy for each parameter significantly reduces energy consumption compared to fixed, inefficient sampling intervals commonly used in commercial weather stations. Furthermore, by employing an energy harvesting system for each sensor, leveraging the light emitted by greenhouse lamps and external sources ensures the autonomy of sensors within the greenhouse, thereby minimizing the need for human intervention for battery replacement and recharging.