Halil Sahin

Halil Sahin is a Research Associate in Machine Learning at the Department of Electrical and Electronic Engineering, University of Manchester. His work bridges advanced image processing techniques with agricultural applications to address food security and sustainability challenges.

Education:

• MSc Digital Signal Processing, University of Manchester (2017-2018), Pass with Merit

Sahin specializes in multi/hyperspectral image analysis for precision agriculture, with core expertise in machine learning-driven plant disease detection, crop-weed segmentation, and stress monitoring. His research directly supports UN Sustainable Development Goals through innovations in sustainable farming and resource optimization. Key methodologies include deep learning architectures like U-Net with CRF enhancements, NDVI analysis, and hyperspectral classification systems.

Recent publications reveal a clear trajectory toward solving critical agricultural data challenges, particularly imbalance data handling in crop monitoring systems and automated plant virus detection. His work integrates computer vision with agricultural science to develop scalable solutions for real-world farming environments.

No scientific awards are documented in current profile information.

Sahin shows no formal student supervision activities in available records. Research grant details are not specified in the current academic profile.

His research operates within the Electrical and Electronic Engineering ecosystem at Manchester, leveraging collaborations with agricultural scientists and computer vision specialists to advance plant phenotyping technologies.