Raji Alahmad

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About

Raji Alahmad is a robotics researcher specializing in computer vision and autonomous systems for agricultural and service applications. Their work focuses on integrating deep learning and multi-sensor perception to enable robots to operate effectively in unstructured, real-world environments. Alahmad’s most impactful contribution is the evaluation of tree branch recognition algorithms for pruning robots, using the YOLOv8-seg model under augmented environmental conditions. This research, which has garnered 4 citations since 2025, addresses a critical bottleneck in agricultural automation: accurate, real-time branch detection amidst variable lighting and occlusion. By rigorously testing the algorithm’s robustness, Alahmad provides a pathway toward more reliable, labor-saving pruning robots that can enhance efficiency and scalability in orchards. Additionally, Alahmad has developed an efficient ball position estimation system for tennis court robot assistants, employing a dual-camera setup to enable autonomous ball collection during professional training sessions. This work, with 2 citations, demonstrates Alahmad’s versatility in applying computer vision to service robotics, reducing human effort and time in sports environments. Through these contributions, Alahmad is advancing the frontier of autonomous robots that can perceive and interact with complex, dynamic surroundings, making them more practical for both agriculture and everyday service tasks.