G C Sunil
Papers
4
Total Citations
69
H-Index
4
About
G.C. Sunil is an emerging researcher at the forefront of precision agriculture, specializing in machine vision, deep learning, and autonomous robotic systems for sustainable weed management. His work addresses one of modern agriculture's most pressing challenges — the overuse of herbicides through blanket spraying — by developing intelligent, site-specific alternatives that dramatically reduce chemical inputs while improving crop health. Sunil's most recognized contributions include the development of a smart sprayer system using edge computing and deep learning, which has already garnered 27 citations since its 2024 publication, and a field-based multispecies weed and crop detection framework employing advanced YOLO architectures (YOLOv8 and YOLOv9), cited 26 times. His data-centric and model-centric approaches across multiple field locations demonstrate both rigor and real-world applicability. Notably, his robotic vehicle software interface, built on YOLOv4, demonstrated the potential to reduce herbicide usage by up to 80%. He has also contributed a dedicated weed-crop dataset to support AI-driven agricultural research. With nearly 70 cumulative citations across recent publications, Sunil is rapidly establishing himself as a key innovator in AI-powered precision agriculture and robotic weed control systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4