Cengiz Koparan
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: Dakota State University (2022); Ankara University (2020); University of Arkansas System (2024–2026); North Dakota State University (2021–2024); Clemson University (2018–2020)
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Cengiz Koparan's research centers on the application of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) to enhance agricultural practices. His work investigates computer-assisted machine systems within the field of precision agriculture, focusing on how these technologies can be utilized to improve agricultural systems. Koparan has explored the use of UAVs for tasks such as thermal infrared and multispectral imaging to detect glyphosate resistance in weed canopies. He has also studied the practical challenges and characteristics of UAVs for precision agriculture applications.
His publications include reviews of current UAV sprayer applications and ground robotic technologies for site-specific weed management. Koparan's work also delves into the use of computer vision and deep learning for classifying weed and crop species, examining algorithm performance under various background conditions and exploring edge computing approaches for smart sprayer systems. He has a publication record of 30 papers with 1,105 citations and an h-index of 14. Koparan collaborates with several researchers at the University of Arkansas at Fayetteville, including Aurelie M. Poncet, Alexander Silva, Margaret Worthington, and Donald M. Johnson, with whom he shares multiple publications.
Metrics
- h-index: 14
- Publications: 30
- Citations: 1,131
Selected Publications
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Smartphone-enabled Depth-aware Transillumination Imaging for Detection of Chicken Breast Fillet Myopathies in Chicken Fillets (2026)
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Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.) (2025)
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A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance (2025)
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Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing (2024)
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Inservice needs of selected Arkansas agriculture teachers related to precision agriculture (2024)
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Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review (2024)
Collaboration Network
Top Collaborators
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Inservice needs of selected Arkansas agriculture teachers related to precision agriculture
- Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.)
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Inservice needs of selected Arkansas agriculture teachers related to precision agriculture
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- Inservice needs of selected Arkansas agriculture teachers related to precision agriculture
- Inservice needs of selected Arkansas agriculture teachers related to precision agriculture
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
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