WISER-AgroTwin: Indo-German Edge Digital Twins for Climate-Resilient Irrigation and Soil-Moisture Intelligence in Smallholder Farms

V. Padmavathi is Professor and Head of Information Technology with over 17 years of teaching experience. She holds a Ph.D. in Computer Science and Engineering from Annamalai University. Her research areas include Artificial Intelligence, Blockchain, IoT, Digital Twin, and Cybersecurity, with multiple SCI/SCIE-indexed publications and funded research projects to her credit. Reference: WISER-AgroTwin is an Indo-German initiative to design, deploy, and field-test edge digital twins to enhance irrigation choices of smallholder farmers in a climate with growing variability. The project will integrate (low-cost) soil and microclimate sensors and satellite measurements, local weather predictions, and just a basic farm journal to produce a constantly updated "virtual farm" that uses estimated root-zone moisture and imminent crop water requirements in real-time, even where internet connectivity is unavailable. The fundamental idea is a hybrid modeling stack that integrates knowledge of water balance and crop physiology with lightweight machine learning to apply sensor fusion, drift correction, and gap filling. On-device inference will produce irrigation advisories that directly account for uncertainty, enabling risk-aware scheduling during heat waves, erratic rainfall, and water rationing. Federated learning. Privacy-preserving federated learning will enable models to improve outcomes across many farms without transferring raw farm data to a central server, enabling greater scale in adoption and ownership of the data. The system will also be developed hand in hand with farmers and extension partners so that it is usable, includes local-language instructions, and integrates with existing irrigation practices practically. Agro-climatic pilots in India and Germany. Multi-site pilots will be used to determine technical performance and actual impact, including estimates of soil moisture, sensor fault resilience, reductions in water and energy consumption, and agronomic benefits (i.e., crop stress and yield stability). Among the deliverables are validated reference architecture, open interfaces for interoperable sensing and decision support, and evidence-based guidelines for deploying climate-smart irrigation in smallholder agriculture. Keywords - Climate-resilient irrigation, Edge digital twin, Soil moisture intelligence, Physics-informed machine learning, federated learning