Mouataz Lghoul
Papers
2
Total Citations
11
H-Index
2
About
Mouataz Lghoul is a researcher at the forefront of digital agriculture and agricultural robotics, specializing in computer vision and autonomous navigation for greenhouse operations. His work addresses critical challenges in precision farming, particularly in developing real-time detection systems and vision-based navigation for small mobile robots. Lghoul’s most cited paper, "Embedding a Real-Time Strawberry Detection Model into a Pesticide-Spraying Mobile Robot for Greenhouse Operation" (2024, 8 citations), demonstrates a practical solution for integrating deep learning models into resource-constrained robotic platforms, enabling efficient fruit detection and targeted pesticide application. His subsequent work, "A Transformation Model for Vision-Based Navigation of Agricultural Robots" (2025, 3 citations), introduces the Top-view Transformation Model (TTM), which eliminates perspective distortions like the vanishing point effect, ensuring uniform spatial representation for reliable autonomous navigation. These contributions are pivotal for advancing smart farming, reducing chemical usage, and improving crop yields. Lghoul’s research bridges the gap between theoretical computer vision and real-world agricultural applications, making him a notable figure in the emerging field of intelligent greenhouse robotics.
Research Focus
Key Achievements
Top Papers
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