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Agriculture is one of the most water-intensive sectors globally, and efficient water management has become a critical requirement for sustainable farming. Traditional irrigation methods often lead to overwatering or underwatering due to manual control and lack of real- time monitoring, resulting in water wastage, reduced crop productivity, and soil degradation. To address these challenges, this paper presents a Digital Twin–Assisted Smart Irrigation System that integrates Internet of Things (IoT) technology with real-time environmental monitoring and predictive analytics. The proposed system utilizes a NodeMCU microcontroller as the core processing unit to continuously monitor soil moisture levels and automate irrigation processes. By combining physical field data with a virtual digital twin model, the system enables intelligent decision-making, ensuring optimal water usage while maintaining healthy crop growth.The architecture of the system consists of soil moisture sensors deployed across the agricultural field to collect real-time data regarding soil water content. These sensors transmit data wirelessly to the NodeMCU, which processes the incoming information and compares it with predefined threshold values based on crop requirements.When the moisture level falls below the desired limit, the system automatically activates water pumps and solenoid valves to initiate irrigation. Once the optimal moisture level is reached, the system turns off the water supply, preventing excess irrigation. This automation eliminates the need for continuous human supervision and ensures precise water delivery tailored to crop needs.A key innovation of this work is the integration of a Digital Twin framework. The digital twin creates a virtual representation of the physical irrigation system, including soil conditions, crop status, environmental parameters, and irrigation equipment. Real-time data from sensors are synchronized with the cloud platform to update the digital model continuously. In addition to real-time soil monitoring, the system incorporates weather prediction data to further optimize irrigation scheduling. By analyzing parameters such as rainfall forecasts, temperature, humidity, wind speed, and evapotranspiration rates, the system intelligently adjusts watering cycles. For instance, if rainfall is predicted, irrigation can be postponed or reduced to conserve water. The cloud-based platform plays a significant role in ensuring scalability and remote accessibility. Data collected from multiple sensor nodes can be stored, analyzed, and visualized on cloud servers, enabling large-scale farm monitoring