An IoT-based approach for microclimate surveillance in greenhouse environments

Abstract

As the demand for efficient and cost-effective greenhouse microclimate surveillance has increased, we developed a microclimate surveillance system using microcontroller technology that automatically collects temperature and relative humidity data and transmits it to a cloud server for remote surveillance and data analysis. 1971 microclimate data points were acquired over a 20-day evaluation period, providing insights into greenhouse environmental conditions with a negative linear regression between air temperature and relative humidity within the greenhouse and an R-squared of 0.73. This illustrates the system’s ability to record and quantify environmental conditions data. Additionally, we derived a predictive model to manage microclimate conditions using the regression formula y = -6.12X + 238.33, where X represents the air temperature and y represents the relative humidity. All the results show that the acquired data can be used to make decisions to optimize plant growth. The prototype we developed can be an alternative option for small and medium-sized farms that need a greenhouse surveillance system to improve operational efficiency and reduce surveillance costs. The system can be further developed by implementing additional sensors to monitor light intensity, wind speed, or soil moisture and further data analysis models to optimize greenhouse management