Max Robotics — Global Service Robot Directory + US Certification

About

Mohamed Ouhda’s research sits at the intersection of artificial intelligence, computer vision, and agricultural technology, with a strong focus on automating date fruit harvesting. His most cited work, the “Date fruit detection dataset for automatic harvesting” (2023, 14 citations), provides a critical resource for training deep learning models to identify and locate date fruits in complex orchard environments. Building on this, his “Smart Harvesting Decision System” (2023, 4 citations) integrates YOLO object detection with K-means segmentation to not only detect fruit but also assess its maturity, enabling precise, real-time harvest decisions that reduce waste and labor costs. Ouhda also contributes to path planning and robotics, as seen in his hybrid Dijkstra’s and A* algorithm with an adaptive threshold heuristic (2023, 9 citations) and his work on multi-agent ant trajectory planning (2024, 2 citations). His exploration of TinyML on Arduino Nano 33 BLE (2024, 2 citations) demonstrates a commitment to deploying AI on low-power edge devices, with applications for assisting disabled individuals. With a growing citation footprint and a clear trajectory toward practical, deployable AI systems, Ouhda is shaping the future of smart agriculture and accessible technology.