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About

Mohammed Algabri is a leading researcher at the intersection of robotics, artificial intelligence, and agricultural technology, whose work has fundamentally advanced autonomous navigation and computer vision systems. His primary research areas encompass mobile robot navigation in unknown environments, soft computing techniques, and deep learning applications for agricultural automation. Algabri’s most impactful contribution is his comparative study of soft computing techniques for mobile robot navigation, which has garnered 95 citations and established foundational methodologies for obstacle avoidance and path planning. He has further pioneered the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and evolutionary learning for self-navigating robots, demonstrating how machines can autonomously adapt to unstructured environments. Notably, his 2020 work on deep learning and computer vision for estimating date fruit type, maturity, and weight—cited 64 times—addresses a critical agricultural challenge, leveraging AI to support Saudi Arabia’s significant date production industry. Through his development of autonomous stereovision-based navigation systems and visual tracking using fuzzy logic, Algabri has consistently pushed the boundaries of intelligent robotics, making his research indispensable for students and engineers working on real-world autonomous systems and precision agriculture.