An Edge Assisted Internet of Things Model for Renewable Energy and Cost-Effective Greenhouse Crop Management

Improved greenhouse Crop Yields (CY) are now within reach due to the rise of "Smart Farming (SF)" based on the Internet of Things (IoT). The IoT presents a massive opportunity for precision farming, which has the potential to increase CY, optimize resource use, and decrease the environmental impact of agriculture. Kenya's climate challenges greenhouse CY, but this paper lays out an integrated model that works well for growing Capsicum there. A multi-layered system equipped with sensors allows for the real-time monitoring of critical Environmental Factors (EF) in the model. For faster responses and less dependence on distant cloud services, these sensors send data to a processing layer that acts as an intermediary and uses Edge Computing (EC) for data management and immediate action. The analytics layer successfully reads sensor data, predicts possible scenarios, and makes decisions using Random Forest (RF) algorithms to improve crop productivity and yield. Also, the framework's user-friendly interface integrates data display and control, enabling efficient human communication. Kenya's climate impedes the cultivation of horticultural crops. The current study demonstrates that a hybrid model using IoT + EC + RF substantially improves Capsicum growth. The research establishes a standard for SF operations by combining advanced data analytics with the IoT to demonstrate how to develop a sustainable and adaptive SF system. This research set the standard for SF production by proving how a dynamic SF environment can be developed by applying advanced analytics with IoT.