IOGRUCloud: A Scalable AI-Driven IoT Platform for Climate Control in Controlled Environment Agriculture
This addresses the problem of inefficient and manual climate management for greenhouse operators in agriculture, representing a domain-specific incremental improvement.
The paper tackles precise climate control in Controlled Environment Agriculture by introducing IOGRUCloud, a scalable IoT platform that integrates AI-driven control with edge computing, resulting in a 73% reduction in manual calibration effort, 23% reduction in energy consumption, and 31% improvement in climate stability across 14 greenhouses.
Controlled Environment Agriculture (CEA) demands precise, adaptive climate management across distributed infrastructure. This paper presents IOGRUCloud, a scalable three-tier IoT platform that integrates AI-driven control with edge computing for automated greenhouse climate regulation. The system architecture separates field-level sensing and actuation (L1), facility-level coordination (L2), and cloud-level optimization (L3-L4), enabling progressive autonomy from rule-based to fully autonomous operation. A Vapor Pressure Deficit (VPD) cascading control loop governs temperature and humidity with GRU-enhanced PID tuning, reducing manual calibration effort by 73%. Deployed across 14 production greenhouses totaling 47,000 m2, the platform demonstrates 23% reduction in energy consumption and 31% improvement in climate stability versus baseline. The system handles 2.3M daily sensor events with 99.7% uptime. We release the architecture specification and deployment results to support reproducibility in smart agriculture research.