Deema Mohammed Alsekait
Papers
1
Total Citations
2
H-Index
1
About
Deema Mohammed Alsekait is a researcher whose work sits at the intersection of artificial intelligence, edge computing, and agricultural technology. Her primary research focus is on developing precise, real-time models for disease detection in crops, leveraging deep learning and data fusion techniques to address critical challenges in food security. Her most notable contribution is the paper "PFDI: A Precise Fruit Disease Identification Model based on Context Data Fusion with Faster-CNN in Edge Computing Environment" (2023), which introduces an innovative framework that combines contextual data with a Faster-CNN architecture, optimized for deployment in resource-constrained edge environments. This work is particularly significant for enabling rapid, on-site diagnosis of fruit diseases without reliance on cloud connectivity, making advanced AI accessible for remote farming communities. While her citation count is still growing, Alsekait's research demonstrates a clear commitment to bridging the gap between cutting-edge AI and practical, deployable solutions in agriculture. Her work stands as a promising foundation for future innovations in precision agriculture and edge-based intelligent systems.
Research Focus
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Top Papers
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