Papers
5
Total Citations
123
H-Index
3
About
Ayan Paul is an emerging researcher at the intersection of agricultural robotics, computer vision, and autonomous systems, with a focused specialization in smart harvesting technologies for greenhouse environments. His most celebrated contribution — a 2024 study applying YOLO-based deep learning for capsicum detection, segmentation, growth stage classification, and real-time mobile identification — has rapidly accumulated 98 citations, signaling its significant influence on precision agriculture research. Paul has further advanced autonomous systems through reinforcement learning-integrated particle swarm optimization for trajectory planning of ground vehicles using 2D LiDAR sensing, demonstrating his breadth across robotics and AI. His more recent work explores the full engineering pipeline of capsicum harvesting robotics, encompassing 6-DOF robotic arm design with hybrid AI optimization, ROS-based kinematic simulation, structural analysis, and DEM-evaluated storage cart dynamics. Together, these contributions represent a coherent and ambitious research agenda aimed at automating one of horticulture's most labor-intensive challenges. Paul's work is particularly notable for bridging theoretical AI frameworks with practical, simulation-validated mechanical solutions, making his research highly relevant to students and engineers pursuing the future of intelligent agricultural automation.
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