Cengiz Koparan

1.1k total citations · 1 hit paper
26 papers, 722 citations indexed

About

Cengiz Koparan is a scholar working on Plant Science, Water Science and Technology and Nature and Landscape Conservation. According to data from OpenAlex, Cengiz Koparan has authored 26 papers receiving a total of 722 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Plant Science, 8 papers in Water Science and Technology and 5 papers in Nature and Landscape Conservation. Recurrent topics in Cengiz Koparan's work include Water Quality Monitoring Technologies (8 papers), Smart Agriculture and AI (7 papers) and Fish Ecology and Management Studies (5 papers). Cengiz Koparan is often cited by papers focused on Water Quality Monitoring Technologies (8 papers), Smart Agriculture and AI (7 papers) and Fish Ecology and Management Studies (5 papers). Cengiz Koparan collaborates with scholars based in United States, Türkiye and Japan. Cengiz Koparan's co-authors include Xin Sun, Calvin B. Sawyer, Charles V. Privette, Sreekala G. Bajwa, Nadia Delavarpour, John Nowatzki, A. Bulent Koc, Ali Koç, Kirk Howatt and Mohammed Raju Ahmed and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing and Computers and Electronics in Agriculture.

In The Last Decade

Cengiz Koparan

24 papers receiving 692 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges

2021 235 citations Nadia Delavarpour, Cengiz Koparan et al. Remote Sensing profile →

Peers

Cengiz Koparan

PS

ECOLO

WST

EE

AE

Bikram Pratap Banerjee Australia

Waldo Ojeda‐Bustamante Mexico

Austin M. Jensen United States

S. Herwitz United States

Shrini K. Upadhyaya United States

Michael J. Starek United States

Dmitrii Shadrin Russia

Ujjwal Verma India

Rocío Ballesteros Spain

Bikram Pratap Banerjee Australia

Cengiz Koparan

209 ×0.7

PS

201 ×1.2

ECOLO

21 ×0.2

WST

191 ×1.4

EE

71 ×0.6

AE

Citations per year, relative to Cengiz Koparan Cengiz Koparan (= 1×) peers Bikram Pratap Banerjee

Countries citing papers authored by Cengiz Koparan

Since Specialization

Citations

This map shows the geographic impact of Cengiz Koparan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Cengiz Koparan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cengiz Koparan more than expected).

Fields of papers citing papers by Cengiz Koparan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cengiz Koparan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Cengiz Koparan. The network helps show where Cengiz Koparan may publish in the future.

Co-authorship network of co-authors of Cengiz Koparan

This figure shows the co-authorship network connecting the top 25 collaborators of Cengiz Koparan. A scholar is included among the top collaborators of Cengiz Koparan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Cengiz Koparan. Cengiz Koparan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Igathinathane, C., et al.. (2025). A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance. Journal of Agriculture and Food Research. 20. 101784–101784.

2.

Zhang, Yu, Cengiz Koparan, Nitin Rai, et al.. (2024). Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review. Computers and Electronics in Agriculture. 225. 109363–109363. 45 indexed citations

3.

Koparan, Cengiz, et al.. (2024). Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing. AgriEngineering. 6(4). 4549–4569. 1 indexed citations

4.

Johnson, Donald M., et al.. (2024). Inservice needs of selected Arkansas agriculture teachers related to precision agriculture. SHILAP Revista de lepidopterología. 5(4). 28–41. 1 indexed citations

5.

Koparan, Cengiz, et al.. (2024). UAV Remote Sensing for Blackberry Flower Intensity Assessment. 1 indexed citations

6.

Koparan, Cengiz, et al.. (2024). A novel automated cloud-based image datasets for high throughput phenotyping in weed classification. Data in Brief. 57. 111097–111097. 1 indexed citations

7.

Koparan, Cengiz, Yu Zhang, Kirk Howatt, et al.. (2024). Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies. 2(2). 63–76. 2 indexed citations

8.

Zhang, Yu, et al.. (2024). 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. Journal of Agriculture and Food Research. 18. 101331–101331. 17 indexed citations

9.

Delavarpour, Nadia, et al.. (2023). A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture. Journal of the ASABE. 66(3). 703–721. 14 indexed citations

10.

Koparan, Cengiz, et al.. (2022). A study on deep learning algorithm performance on weed and crop species identification under different image background. Artificial Intelligence in Agriculture. 6. 242–256. 23 indexed citations

11.

Zhang, Yu, et al.. (2022). Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. Journal of Agriculture and Food Research. 9. 100325–100325. 68 indexed citations

12.

Ahmed, Mohammed Raju, et al.. (2022). Multiclass Classification on Soybean and Weed Species Using a Novel Customized Greenhouse Robotic and Hyperspectral Combination System. SSRN Electronic Journal. 1 indexed citations

13.

Koparan, Cengiz, et al.. (2021). UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection. Remote Sensing. 13(22). 4606–4606. 24 indexed citations

14.

Koparan, Cengiz, A. Bulent Koc, Calvin B. Sawyer, & Charles V. Privette. (2020). Temperature Profiling of Waterbodies with a UAV-Integrated Sensor Subsystem. Drones. 4(3). 35–35. 12 indexed citations

15.

Koparan, Cengiz, A. Bulent Koc, Charles V. Privette, & Calvin B. Sawyer. (2020). Adaptive Water Sampling Device for Aerial Robots. Drones. 4(1). 5–5. 31 indexed citations

16.

Koparan, Cengiz, A. Bulent Koc, Charles V. Privette, & Calvin B. Sawyer. (2019). Autonomous In Situ Measurements of Noncontaminant Water Quality Indicators and Sample Collection with a UAV. Water. 11(3). 604–604. 34 indexed citations

17.

Koparan, Cengiz, et al.. (2018). Evaluation of a UAV-Assisted Autonomous Water Sampling. Water. 10(5). 655–655. 58 indexed citations

18.

Koparan, Cengiz & A. Bulent Koc. (2017). <i>Development of a UAV-Assisted Water Quality Measurement System</i>. 2017 Spokane, Washington July 16 - July 19, 2017. 2 indexed citations

19.

Koc, A. Bulent, et al.. (2017). <i>Laboratory evaluation of high-frequency vibrations on soil cutting force </i>. 2017 Spokane, Washington July 16 - July 19, 2017. 2 indexed citations

20.

Koparan, Cengiz & A. Bulent Koc. (2016). Unmanned Aerial Vehicle (UAV) assisted water sampling. TigerPrints (Clemson University). 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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