C
Cengiz Koparan Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
faculty
14 h-index
29 pubs
1,080 cited
Research Areas
Links
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) in precision agriculture. His work investigates computer-assisted agricultural machine systems, aiming to improve agricultural practices and management through the integration of these technologies. Koparan has published studies on UAV characteristics for precision agriculture, including practical challenges and sprayer applications. His research also explores the use of computer vision and deep learning for weed and crop species classification, as well as thermal infrared and multispectral imaging for detecting herbicide resistance in crops. He has developed and evaluated smart sprayer systems utilizing machine vision and deep learning for site-specific weed management, incorporating edge computing approaches. Koparan's scholarship metrics include an h-index of 14, with 29 total publications and 1,057 total citations. He collaborates with researchers at the University of Arkansas at Fayetteville, including Aurelie M. Poncet, Alexander Silva, Margaret Worthington, and Donald M. Johnson.
Metrics
- h-index: 14
- Publications: 29
- Citations: 1,080
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
Kirk Howatt
North Dakota State University (US)
10 shared publications
- Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
- A study on deep learning algorithm performance on weed and crop species identification under different image background
- Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions
Showing 5 of 10 shared publications
Xin Sun
North Dakota State University (US)
8 shared publications
- A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges
- Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
- A study on deep learning algorithm performance on weed and crop species identification under different image background
Showing 5 of 8 shared publications
Sreekala G. Bajwa
Montana State University (US)
5 shared publications
- A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture
- Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions
- Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies
Mohammed Raju Ahmed
North Dakota State University (US)
5 shared publications
- Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
- A study on deep learning algorithm performance on weed and crop species identification under different image background
- Multiclass Classification on Soybean and Weed Species Using a Customized Greenhouse Robotic and Hyperspectral Combination System
- Multiclass Classification on Soybean and Weed Species Using a Novel Customized Greenhouse Robotic and Hyperspectral Combination System
- A novel automated cloud-based image datasets for high throughput phenotyping in weed classification
G C Sunil
North Dakota State University (US)
4 shared publications
- Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
- A study on deep learning algorithm performance on weed and crop species identification under different image background
- Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach
- A novel automated cloud-based image datasets for high throughput phenotyping in weed classification
Michael Ostlie
Carrington College (US)
3 shared publications
- UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
- Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions
- Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies
Yu Zhang
North Dakota State University (US)
3 shared publications
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- A study on deep learning algorithm performance on weed and crop species identification under different image background
- Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach
Kelvin Betitame
North Dakota State University (US)
3 shared publications
- A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture
- Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies
- A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance
Arjun Upadhyay
North Dakota State University (US)
3 shared publications
- Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review
- Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach
- A novel automated cloud-based image datasets for high throughput phenotyping in weed classification
Aurelie M. Poncet
University of Arkansas System
3 shared publications
In database
- 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.)
Nadia Delavarpour
North Dakota State University (US)
2 shared publications
- A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges
- A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture
Austin Eide
North Dakota State University (US)
2 shared publications
- UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
- Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions
Billy G. Ram
2 shared publications
- Multiclass Classification on Soybean and Weed Species Using a Customized Greenhouse Robotic and Hyperspectral Combination System
- Multiclass Classification on Soybean and Weed Species Using a Novel Customized Greenhouse Robotic and Hyperspectral Combination System
Yu Zhang
2 shared publications
- Multiclass Classification on Soybean and Weed Species Using a Customized Greenhouse Robotic and Hyperspectral Combination System
- Multiclass Classification on Soybean and Weed Species Using a Novel Customized Greenhouse Robotic and Hyperspectral Combination System
Yu Zhang
North Dakota State University (US)
2 shared publications
- Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions
- A novel automated cloud-based image datasets for high throughput phenotyping in weed classification
Similar Researchers
Based on overlapping research topics
Christopher J. Heffernan
Arkansas Agricultural Experiment Station
Remote Sensing in Agriculture
Machine Learning Applications
Agronomic Practices and Intercropping Systems
L. Lanier Nalley
University of Arkansas at Fayetteville
Remote Sensing in Agriculture
Machine Learning Applications
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Richard G. Ham
University of Arkansas at Fayetteville
Remote Sensing in Agriculture
Machine Learning Applications
Agronomic Practices and Intercropping Systems
Rodger Farr
University of Arkansas at Fayetteville
Remote Sensing in Agriculture
Weed Control and Herbicide Applications
Agronomic Practices and Intercropping Systems
L. Nieto
Arkansas Agricultural Experiment Station
Remote Sensing in Agriculture
Agronomic Practices and Intercropping Systems
Plant Physiology and Cultivation Studies
V. Steven Green
Arkansas State University
Remote Sensing in Agriculture
Machine Learning Applications
Agronomic Practices and Intercropping Systems