Intellectual Property India Publishes Patent Application for Ai-Enabled Smart Greenhouse Digital Twin System For Real-Time Monitoring, Prediction, And Intelligent Irrigation Control Using Iot And Embedded Systems.

MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108142 A) filed by St. Joseph'S College Of Engineering; Kamali Sri. K; Oviya. S; and Dr. Ramesh Babu M on September 09, 2026, for Ai-Enabled Smart Greenhouse Digital Twin System For Real-Time Monitoring, Prediction, And Intelligent Irrigation Control Using Iot And Embedded Systems..

Inventors include Kamali Sri. K; Oviya. S; and Dr. Ramesh Babu M.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: Smart agriculture has become increasingly important due to rising global food demand, climate variability, and the need for efficient resource utilization. Traditional greenhouse monitoring systems are limited to data acquisition and visualization and lack predictive intelligence and system-level simulation capabilities. This paper presents an AI-enabled Smart Greenhouse Digital Twin System that integrates IoT sensors, embedded hardware, and a Python-based virtual model to create a real-time synchronized cyber-physical environment. The proposed system continuously collects environmental parameters such as temperature, humidity, and soil moisture using IoT sensors and transmits them to a digital twin engine implemented in Python. The digital twin replicates the physical greenhouse behavior using a hybrid modeling approach combining environmental dynamics and data-driven prediction. It estimates future soil moisture conditions and plant water requirements, enabling proactive irrigation control and system optimization. A closed-loop feedback mechanism is implemented where the digital model not only visualizes real-time greenhouse conditions but also predicts future states and triggers actuator responses such as irrigation pumps and ventilation control. The system further enhances decision-making through predictive analytics and simulation-based “what-if” scenarios, allowing evaluation of different environmental conditions on crop health. Experimental results demonstrate that the proposed digital twin framework improves monitoring accuracy, reduces water wastage, and enables intelligent automation compared to conventional IoT-based monitoring systems. This work highlights the potential of digital twin technology as a next-generation solution for smart greenhouse management and precision agriculture applications

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