Optimizing Resource Efficiency in Smart Greenhouses Through IoT

In the pursuit of sustainable agriculture, smart greenhouses have emerged as a vital solution, enhancing crop productivity while assuring resource conservation. This research delves into the optimization of resource efficiency in smart greenhouses by leveraging the Internet of Things (IoT). Through a comprehensive review of current technologies and methods, the study identifies key challenges and opportunities within the domain. A novel framework is proposed, integrating IoT sensors and actuators to monitor and control the microclimate, ensuring optimal growth conditions while minimizing resource usage. Data from sensor networks are analyzed to predict resource needs, enabling precision farming practices. The framework emphasizes the use of Machine Learning algorithms to enable predictive analytics for resource allocation, ensuring that water, nutrients, and energy are distributed more efficiently. The potential of Lightweight Resource Allocation (LaRA) algorithms is also explored to maintain the delicate balance between maximizing yield and minimizing environmental impact. Ultimately, this research highlights the transformational potential of IoT in creating green, efficient, and productive agricultural practices within smart greenhouses.