IoT and Edge-AI Enabled Vertical Farming Framework for Energy-Efficient Urban Horticulture Applications

Abstract

The demand for sustainable urban horticulture systems has grown due to urbanization and loss of arable area and rising food demand. While conventional systems lack real-time intelligence, are energy intensive, delay an environmental response, and are underutilizing resources, vertical farming has proven a viable solution. Other cloud based agricultural systems are challenged by latency, bandwidth requirements, and scalability. In this paper, a vertical farming framework using the IoT and Edge-AI is proposed to achieve energy efficient vertical agriculture. The framework incorporates IoT sensors that track the temperature, humidity, soil moisture, CO₂ concentration and light intensity. A decision engine for learning embedded in edge devices enables real-time irrigation, ventilation and smart LED lighting predictions and control, with negligible latency. The prime achievements are the design of a scalable IoT-edge architecture, the design of an intelligent low-latency control mechanism, the formulation of an energy-aware optimization model, and the comprehensive performance evaluation based on the actual data from the sensors. The findings of the experiment show that energy consumption is reduced, environmental stability is improved, the response time is quicker, water use is optimized, and productivity of the crop is improved; which makes the framework applicable to the next generation smart urban horticulture systems with sustainable energy consumption.