Papers
3
Total Citations
118
H-Index
3
About
Ambuj is pioneering the intersection of artificial intelligence and agricultural robotics, with a sharp focus on automating precision farming. His most impactful work, “Smart solutions for capsicum Harvesting,” has garnered 98 citations by introducing a YOLO-based framework that simultaneously handles detection, segmentation, growth stage classification, counting, and real-time mobile identification—a comprehensive leap toward fully autonomous harvesting. Beyond computer vision, Ambuj advances autonomous ground vehicle (AGV) navigation through reinforcement learning and particle swarm optimization (PSO), as seen in his trajectory planning paper (14 citations) that leverages 2D LiDAR point clouds for robust pathfinding. His 2024 study on optimizing energy expenditure in AGVs (6 citations) tackles a critical bottleneck: accurate energy consumption prediction. By proposing a GPU-accelerated PSO-artificial neural network framework, he dramatically reduces computational demands while improving prediction accuracy, enabling longer, more efficient field operations. This work directly addresses the high computational overhead that has historically limited real-world deployment of intelligent agricultural systems. Ambuj’s contributions are not merely technical—they are foundational to a future where farms operate with minimal human intervention, maximizing yield while minimizing resource waste.
Research Focus
Key Achievements
Top Papers
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