Rahul Mapari

Papers

Total Citations

H-Index

About

Rahul Mapari is an emerging researcher in agricultural robotics and computer vision, focusing on automating labor-intensive farming tasks. His work centers on two key areas: precision crop detection and autonomous weed/insect control systems. Mapari’s most cited paper, “Cotton Detection Using YOLOv5” (2024, 3 citations), tackles the challenge of accurately identifying cotton blooms amidst leaf obstructions—a critical step toward replacing manual harvesting with robotic systems. This work addresses inconsistencies in yield and quality caused by traditional harvesting methods. His earlier review, “Automatic Weed Killing Robot for Agriculture Purpose and Insect Killing” (2019, 2 citations), comprehensively surveys autonomous weed control, highlighting that plant detection and crop-versus-weed classification remain the greatest technical hurdles. Mapari’s contributions underscore the need for robust computer vision algorithms in precision agriculture, particularly for distinguishing crops from weeds in complex field environments. While his citation counts are modest, his research lays foundational groundwork for scalable, automated farming solutions. By integrating deep learning with robotics, Mapari is helping to advance sustainable agriculture, reducing reliance on manual labor and chemical herbicides. His work is particularly relevant for students and researchers interested in the intersection of AI, robotics, and agritech.