Roobaea Alroobaea

Papers

Total Citations

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About

Roobaea Alroobaea is a researcher working at the intersection of artificial intelligence, edge computing, and smart agriculture, with a growing focus on practical applications of deep learning and IoT technologies in real-world environments. His most impactful contribution, the PFDI model, introduces a precise fruit disease identification framework leveraging context data fusion with Faster-CNN in edge computing settings, earning 28 citations and demonstrating his expertise in deploying intelligent computer vision systems for agricultural diagnostics. Building on this foundation, his AI-IoT-based smart agriculture pivot work, which has garnered 23 citations, addresses critical modern farming challenges — including water scarcity, plant diseases, and pest management — by integrating artificial intelligence with Internet of Things infrastructures to advance smart farming use cases. His research extends further into robotics and cloud systems, where he explores Markov decision processes combined with deep reinforcement learning to optimize data offloading for resource-constrained robotic platforms. Collectively, Alroobaea's work reflects a coherent research vision: harnessing advanced machine learning architectures and distributed computing paradigms to solve tangible technological and agricultural problems, making him a noteworthy contributor to the applied AI and precision agriculture research communities.