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 (PLSC, 2023 - Now)
PhD (PLSC, 2025 - Now)
PhD (ME, 2026 - Now)
PhD (PLSC, 2026 - Now)
MS (ME, 2026 - Now)
MS (Robotics, 2026 - Now)
MS (Robotics, 2026 - Now)
MS (Robotics, 2026 - Now)
MS (Robotics, 2026 - Now)
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 2026: Vision Guided Watermelon Harvesting with Robotic Fruit Handling
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