Berat Can CEYLAN
Papers
1
Total Citations
4
H-Index
1
About
Berat Can Ceylan is a researcher at the forefront of agricultural robotics and artificial intelligence, with a focused expertise in applying deep learning to precision agriculture and automated harvesting systems. His most cited work, "Application of YOLOv8L Deep Learning in Robotic Harvesting of Persimmon (Diospyros kaki)" (2023), demonstrates a significant contribution to the field by integrating state-of-the-art object detection models into real-world agricultural challenges. This study, which has garnered 4 citations, highlights how deep learning techniques outperform traditional methods in analyzing and processing agricultural data, achieving high accuracy for fruit detection and localization. Ceylan’s research bridges the gap between computer vision and robotics, offering practical solutions for automating the harvesting of delicate crops like persimmon. His work is notable for advancing the use of YOLOv8L—a cutting-edge deep learning architecture—in agricultural contexts, showcasing its potential to enhance efficiency and reduce labor dependency. By pushing the boundaries of AI-driven automation, Ceylan is helping to shape the future of smart farming, making his research highly relevant for students and researchers exploring the intersection of deep learning, robotics, and sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1