Environment Prediction System for Smart Greenhouses using IoT-based Spatial Monitoring Applications

This study introduces a data-driven climate prediction model for greenhouses, leveraging a cost-effective Internet of Things (IoT) spatial monitoring system. The IoT infrastructure, comprising data collection, transmission, analysis, and visualization stages, was deployed and tested in a real greenhouse over three months, focusing on temperature, humidity, CO2 levels, and rain levels. The model demonstrated a 24-hour prediction advantage, enhancing accuracy and enabling adjustments for optimal plant growth conditions. A spatial monitoring strategy based on packing density theory addresses climate variability within greenhouses striking a balance between effectiveness and cost. Real-world testing validated the system’s efficacy, making it accessible to a wide range of greenhouse operators. The integration of IoT technology in agriculture presents a significant step towards sustainable and efficient practices to meet the rising global demand for food.