Smart Greenhouse Using Ai And Iot: A Review Of Climate Sensing, Automated Control And Predictive Microclimate Management

A smart greenhouse is an IoT-based automated agriculture system that maintains suitable conditions for plant growth while reducing water, energy and labour. Conventional greenhouses depend on continuous human monitoring and manual switching of pumps, fans and lights, which wastes resources and allows stress conditions to go unnoticed. This paper reviews AI- and IoT-enabled smart greenhouses. It examines sensing of temperature, humidity, soil moisture, light intensity and carbon dioxide; microcontroller platforms such as Arduino and ESP32 that drive irrigation pumps, exhaust fans, foggers and grow lights; and communication through Wi-Fi and MQTT to cloud dashboards and mobile applications for remote monitoring. Beyond fixed-threshold control, it reviews machine learning that predicts the greenhouse microclimate a few hours ahead, recurrent networks for time-series forecasting, and AI-based climate and irrigation strategies that have competed with expert growers. A five-layer architecture comprising sensing, edge control, connectivity, cloud intelligence and user application is proposed, along with research gaps, objectives, a comparison of control strategies and challenges such as sensor drift, power failures, energy cost and crop-specific tuning. The review concludes that predictive, AI-assisted control can make greenhouses more productive and resource-efficient than threshold-based automation alone.