Rahul Harsha Cheppally

Assistant Professor

Contact information

Education

Ph.D., Biological and Agricultural Engineering, Kansas State University, 2024
M.S., Mechanical Engineering, Cleveland State University, 2017

Professional experience

Rahul Harsha Cheppally is a robotics and artificial intelligence researcher with experience spanning autonomous systems, computer vision, machine learning, agricultural robotics and real-time edge deployment. He is an assistant professor at Kansas State University, where his work focuses on developing intelligent agricultural machinery through perception, navigation, multimodal sensing and embodied AI. His research includes end-to-end transformer architectures for crop-row detection, large-scale vision-model distillation, 3D reconstruction, robotic localization and autonomous navigation, with systems deployed on platforms such as NVIDIA Jetson, ROS 2 and GPU-accelerated computing environments. He has led robotics development at K-State’s Farms Lab, collaborated with industry partners on field-deployable agricultural autonomy systems, and previously worked as a positioning engineer at Caterpillar.

Research

A central theme of Cheppally’s research is bridging advances in modern artificial intelligence with practical agricultural robotics by developing intelligent systems that can operate reliably in complex and changing field environments. His work emphasizes the design of computationally efficient perception, learning and autonomy models that can be deployed directly on embedded and edge-computing platforms, enabling real-time decision-making without relying on large-scale computing infrastructure. By combining modern AI techniques with robotic sensing, control and field deployment, his research targets applications such as autonomous navigation, precision spraying, crop monitoring and intelligent agricultural machinery. The broader goal is to translate advances in AI into robust, scalable and deployable robotic technologies that improve the efficiency, precision and autonomy of agricultural operations.

Academic highlights

Cheppally’s research has resulted in peer-reviewed publications about combines fundamental AI research with practical robotic systems designed to operate reliably in challenging real-world agricultural environments.