Gugan SK

Vellore Institute of Technology University

Papers

Total Citations

H-Index

About

Gugan SK is a rising researcher in the field of precision agriculture and deep learning, whose work focuses on the automated detection and classification of weeds in farmlands. His most-cited paper, "Weed Detection in Farmlands Using RCNN" (2025), introduces a region-based convolutional neural network approach to identify weed species, enabling targeted removal and reducing competition for crop nutrients. This contribution addresses a critical bottleneck in sustainable farming—optimizing herbicide use and improving crop yields through computer vision. With 4 citations in a short time, his work is gaining traction among agritech and AI researchers. Gugan’s research stands out for its practical application of deep learning to real-world agricultural challenges, bridging the gap between advanced neural architectures and on-field deployment. As a young researcher, his early impact signals a promising trajectory in developing intelligent systems for environmental monitoring and resource-efficient farming.