About Me
I build sensing-driven, AI-powered systems that help growers see what the field is saying long before the season does. My work turns satellite imagery, drone data, and spatial machine learning into practical tools that farmers can use to detect stress early, understand crop variability, and make confident decisions.
I’m currently developing next-generation digital agriculture models that combine multisensor data, explainable AI, and spatial reasoning for real-world impact.
Precision Agriculture | AI | Remote Sensing Virginia Tech
I am a PhD candidate in Precision Agriculture and Artificial Intelligence at Virginia Tech, where I focus on creating operational, science-based solutions for crop monitoring and yield forecasting. My interests grew from years of working with Earth observation data across India, the Netherlands, and the United States.
My background spans remote sensing, geospatial science, machine learning, and agronomy. I work closely with growers, agronomists, and research teams to turn complex satellite and UAV datasets into field-ready insights. I enjoy building systems that feel intuitive for farmers yet remain scientifically rigorous and transparent.
Across my projects, a single theme drives my work: make advanced sensing and AI genuinely useful for agriculture—not just impressive on paper.