Mohammad Albaroudi
Papers
2
Total Citations
6
H-Index
2
About
Mohammad Albaroudi is a forward-thinking researcher at the intersection of robotics, computer vision, and agricultural automation. His work centers on developing intelligent robotic systems capable of perceiving and interacting with complex, unstructured environments. Albaroudi’s most significant contribution is his evaluation of the YOLOv8-seg algorithm for tree branch recognition in pruning robots under augmented environmental conditions. This research, which has already garnered 4 citations since its 2025 publication, addresses a critical bottleneck in agricultural robotics: the accurate detection of branches amidst varying light, occlusion, and foliage. By rigorously testing deep learning models in challenging field conditions, he provides a pathway toward fully autonomous, precision pruning—a labor-intensive task vital for orchard management. In parallel, Albaroudi has advanced sports robotics with his work on efficient ball position estimation for tennis court assistants, using a dual-camera system to achieve robust, real-time ball tracking. This innovation, cited 2 times, tackles the practical challenge of automating ball collection during professional training, saving both time and energy. Through his focused application of state-of-the-art segmentation and multi-sensor fusion, Albaroudi is shaping the next generation of robots that work alongside humans in agriculture and sports.
Research Focus
Key Achievements
Top Papers
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- 2