Erenus Yildiz
Papers
2
Total Citations
18
H-Index
2
About
Erenus Yildiz is a researcher at the intersection of computer vision, robotics, and agricultural AI, with a focus on applying deep learning to real-world automation and phenotyping tasks. His most cited work, "A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines" (2020, 16 citations), tackles the challenge of robotic manipulation in industrial recycling by using state-of-the-art deep learning models to analyze visual scenes for precise disassembly—a contribution that advances sustainable automation and circular economy practices. In a more recent contribution, Yildiz led the development of a dataset and baseline method for "Deep learning based 3d reconstruction for phenotyping of wheat seeds" (2023, 2 citations), addressing a critical need in plant science: measuring seed shape and volume from images to inform early plant development studies. This work bridges computer vision and agriculture, offering tools for high-throughput phenotyping. Yildiz’s research demonstrates a commitment to deploying AI in tangible, high-impact domains—from e-waste recycling to crop science—showcasing how deep learning can solve practical problems with measurable societal and environmental benefits. His growing citation record reflects the relevance of his applied vision research.
Research Focus
Key Achievements
Top Papers
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