Our research focuses on automation technology for facilitating scientific discoveries and advancing production systems in agriculture. Specifically, we leverage sensors, multimodal imaging, machine/deep learning-based predictive models, unmanned ground/aerial vehicles, and robotics to develop reliable, affordable, and efficient tools for plant phenomics and precision farming.
Advancing digital agriculture through imaging, robotics, and AI.

AI-powered vision-based seedling counting and quality assessment for forest nursery.

Smartphone app to detect ripe berries and predict yield.

Automate lima bean pod counting using robotic multi-view imaging, 3D Gaussian Splatting, and Segment Anything Model.

Aerial phenotyping of morphology and physiology using drone-based hyperspectral imaging and LiDAR.
Assistant Professor
PhD (2019 - 2023)
Corteva Agriscience
MS (2021 - 2023)
Optix Technologies
MS (2021 - 2023)
Cornell University
MS (2020 - 2022)
Qlik
MS (2020 - 2022)
Michigan State University (PhD)
MS (2020 - 2021)
Nordstrom
Farm Robotic Challenge 2025: Monitoring Saltwater Intrusion in Coastal Farms Using Drones & Autonomous Robots
SLAM Navigation of Farm-ng Amiga Robot using Mid 360 LiDAR
An advanced agricultural management system.
Aerial surveillance and data collection.
Robotic platform for center pivot irrigation system.
Powerful industrial AI gateway for field robotics.
Next-gen AI computing for autonomous machines.
Compact and high performance embedded system for stereo vision processing.
Cubert X20P Snapshot UV-VIS-NIR Hyperspectral Video Camera.
For more precise, efficient, and reliable geospatial data acquisition.
Compact and light weight LiDAR for navigation and obstacle avoidance.
Compact and precise for depth sensing applications.
Flexible integration and customization of stereo systems.
High intensity strobe lights for synchronized lighting.
RTK GNSS receiver with centimeter accuracy upto 60° tilt.
Measures Soil Moisture, EC, and Surface Temperature.
Medusa MS-350 for soil composition analysis.