Shorouk Ramadan
Papers
1
Total Citations
4
H-Index
1
About
Shorouk Ramadan is a researcher at the forefront of applying deep learning to agricultural automation, with a particular focus on date fruit classification. Her most cited work, "Date Fruit Classification System using Deep Transfer Learning" (2023), addresses a critical challenge in smart agriculture: developing accurate computer vision systems for robotic harvesting. By leveraging deep transfer learning, Ramadan's system can automatically classify both the type and maturity stage of date fruit—a task essential for designing efficient, intelligent harvesting solutions. Though early in her citation trajectory with 4 citations to this paper, the work signals a promising contribution to precision agriculture and food technology. Her research sits at the intersection of computer vision, deep learning, and agricultural engineering, aiming to reduce manual labor and improve yield quality through automation. Ramadan's approach demonstrates how transfer learning can overcome data scarcity in specialized agricultural domains, making advanced AI accessible for niche crops. As the demand for smart farming solutions grows, her work provides a foundational framework for future robotic harvesting systems, positioning her as an emerging voice in applied deep learning for sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1Date Fruit Classification System using Deep Transfer Learning4 citations · 2023