Research Article
Digital Twin Based Soil Moisture Measurement and Irrigation Optimization System
@INPROCEEDINGS{10.4108/eai.19-12-2025.2363631, author={Arvinder Kaur and Pardeep Kumar and Prashant Yadav and Geeta Yadav}, title={ Digital Twin Based Soil Moisture Measurement and Irrigation Optimization System }, proceedings={Proceedings of the 1st International Conference on Computational Techniques in Data Science, IICTDS 2025, 19-20 December 2025, Chandigarh, India}, publisher={EAI}, proceedings_a={IICTDS}, year={2026}, month={11}, keywords={Sustainable Smart irrigation Virtual reality Digital Twin}, doi={10.4108/eai.19-12-2025.2363631} }- Arvinder Kaur
Pardeep Kumar
Prashant Yadav
Geeta Yadav
Year: 2026
Digital Twin Based Soil Moisture Measurement and Irrigation Optimization System
IICTDS
EAI
DOI: 10.4108/eai.19-12-2025.2363631
Abstract
Digital twin technology has emerged as an effective solution for precision agriculture and water conservation. A Digital Twin based soil moisture measurement and irrigation optimization system is presented. The proposed techniques integrates multiple environment sensors, real-time monitoring and intelligent decision-making for efficient irrigation schedule. The proposed method uses sensos to collect data and process through Deep Forest-Based machine learning techniques for predicting soil moisture and irrigation requirements. The Deep Forest and Digital Twin methodology achieves overall prediction accuracy of 94.8% with irrigation success rate of 95.6%. The proposed approach offers a reliable, scalable and intelligent solution for sustainable agriculture, efficient water management and enhanced crop productivity.