With the upsurge in demand for the sustainable agriculture practices, the development of IoT driven smart greenhouses has become very popular as it not only increases the crop production but also reduces resource use. But the current practice of greenhouse management is usually limited to real-time monitoring of few environmental parameters and energy intensive climate control methods resulting in conditions suboptimal for plant growth. The goal of this research is to build smart greenhouse system where the environmental parameters are temperature, humidity, soil moisture and light levels are monitored in real time using IoT. The system achieves improved environmental regulation and resource management through integrating the sensors with a cloud based IoT solution. The increase in greenhouse efficiency is achieved in part, through rule based control and fuzzy logic as well as machine learning based predictive methods. Experimental results indicate that integration of cloud and machine learning based predictive control results in optimal environmental conditions which leads to 30 percent reduction in the water use and 25 percent reduction in the energy consumption compared to conventional methods. Utilizing this scalable system means it has provided a solution that is modern and sustainable for greenhouse operations, leading to better overall agricultural efficiency and the pursuit of smarter farming methods.
Mondal et al. (Fri,) studied this question.
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