Chetan Badgujar
University of Tennessee at Knoxville, Kansas State University
Papers
8
Total Citations
383
H-Index
5
About
Chetan Badgujar is an emerging researcher at the forefront of agricultural robotics, autonomous systems, and precision agriculture technology. His work focuses on integrating cutting-edge computer vision, machine learning, and robotic platforms to address critical challenges in modern farming, from pest management to terrain navigation. Badgujar's most influential contribution is his comprehensive bibliometric and systematic literature review on the YOLO (You Only Look Once) object detection algorithm in agricultural applications, which has garnered an remarkable 326 citations since 2024, establishing him as a leading voice in agricultural computer vision. This work synthesizes the rapidly growing body of research applying real-time detection frameworks to crop and pest identification. Beyond computer vision, Badgujar has made notable contributions to autonomous ground vehicle design, developing deep neural network models for predicting vehicle behavior on challenging sloped terrain — addressing the serious safety risks of conventional machinery on hillsides. His work on reconfigurable crop scouting vehicles, robotic wheat drills, and invasive tree-cutting robots demonstrates a versatile engineering approach to farm automation. His distributed coverage path planning algorithm further highlights his systems-level thinking for multi-robot agricultural deployment. With over 380 cumulative citations, Badgujar's research is shaping the future of smart, autonomous, and safer farming systems.
Research Focus
Key Achievements
Top Papers
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- 6Development and Evaluation of PastureTree Cutting Robot5 citations · 2022
- 7Self-Service Innovations in Precision Agriculture3 citations · 2025
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