Cengiz Koparan
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: Ankara University (2020); University of Arkansas System (2024–2026); North Dakota State University (2021–2024); Clemson University (2018–2020)
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Cengiz Koparan's research focuses on the application of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) to enhance agricultural practices and management. His work investigates computer-assisted machine systems within the context of precision agriculture, aiming to improve the overall efficiency and effectiveness of farming operations.
Koparan has published research on the technical characteristics of UAVs for precision agriculture, including practical challenges associated with their use. His publications also explore in situ water quality measurements using UAV systems and the evaluation of UAV-assisted autonomous water sampling. Additionally, he has investigated weed and crop species classification using computer vision and deep learning, as well as advancements in ground robotic technologies for site-specific weed management. His work also includes the development of adaptive water sampling devices for aerial robots and the use of UAV-assisted imaging for detecting glyphosate resistance in weed canopies.
His scholarly contributions include 33 publications with 1,172 citations and an h-index of 14. Koparan collaborates with researchers at the University of Arkansas at Fayetteville, including Aurelie M. Poncet, Alexander Silva, Margaret Worthington, and Dongyi Wang, with whom he has co-authored multiple publications.
Metrics
- h-index: 14
- Publications: 33
- Citations: 1,175
Positions
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Assistant Professor 2023–presentUniversity of Arkansas at Fayetteville Agricultural Education, Communications and Technology ORCID
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Postdoctoral Research Associate 2020–2023North Dakota State University Agricultural and Biosystems Engineering ORCID
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Graduate Research Assistant 2015–2020Clemson University Agricultural Sciences ORCID
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)
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.)
- Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
- Inservice needs of selected Arkansas agriculture teachers related to precision agriculture
- 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
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
- Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.)
- Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.)
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