Hajar Mousannif
Papers
6
Total Citations
24
H-Index
4
About
Hajar Mousannif is a leading researcher at the intersection of artificial intelligence, robotics, and precision agriculture. Her work spans reinforcement learning, autonomous navigation, and intelligent weed detection, with a focus on developing practical, cost-effective solutions for real-world challenges. She has made major contributions to autonomous mobile robotics, including the development of an adaptive model predictive control system for 4WD-4WS robots that integrates multivariate Gaussian mixture models and ant colony optimization for robust trajectory tracking and obstacle avoidance. In precision agriculture, Mousannif pioneered a data fusion-based weed detection system and introduced a novel navigation system for holonomic agricultural robots using Interval Type-2 fuzzy logic. Her recent work on enhancing weed detection through knowledge distillation and attention mechanisms, as well as implementing low-cost vision methods for autonomous navigation, demonstrates her commitment to making advanced agricultural technologies accessible in developing countries. With over 20 citations to her most-cited papers, including a comprehensive review of reinforcement learning algorithms from Q-learning to PPO, Mousannif’s research is shaping the future of intelligent, autonomous systems in agriculture and robotics.
Research Focus
Key Achievements
Top Papers
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