Abdullah M. Baqasah

Papers

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About

Abdullah M. Baqasah is an emerging researcher specializing in edge computing, deep learning, and agricultural technology, with a particular focus on applying artificial intelligence to real-world detection and classification challenges. His most notable contribution, the PFDI (Precise Fruit Disease Identification) model, demonstrates his expertise in integrating context data fusion with Faster-CNN architectures to deliver accurate, computationally efficient disease detection in edge computing environments. This work addresses a critical need in agricultural science, where citrus and other commercially significant fruits remain highly vulnerable to infectious diseases that threaten crop yields and food security. By deploying deep learning solutions within edge computing frameworks, Baqasah bridges the gap between advanced machine learning research and practical, resource-constrained agricultural applications — a contribution of growing importance as smart farming technologies become increasingly essential. With 28 citations, his 2023 publication has already attracted meaningful attention from the research community, signaling genuine impact in both the computer vision and precision agriculture domains. His work positions him as a promising voice at the intersection of intelligent systems, embedded computing, and sustainable food technology.