Tam Sobeih
Impact in
- Analytical Chemistry top 5%
- Spectroscopy and Chemometric Analyses
- Plant Science top 10%
- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
- Date Palm Research Studies
- Plant Disease Management Techniques
Papers in
-
- Smart Agriculture and AI 3
- Ecology 2
- Remote Sensing in Agriculture 2
- Co-authors
- Xin Zhang (3 shared papers)Prof. Liangxiu Han (7 shared papers)Lianghao Han (4 shared papers)Yue Shi (1 shared paper)Huiqin Ma (1 shared paper)Pablo González‐Moreno (1 shared paper)Yingying Dong (1 shared paper)Huichun Ye (1 shared paper)
- Journals
- Remote Sensing (2 papers)Neurocomputing (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)Lecture notes in computer science (1 paper)Smart Agricultural Technology (1 paper)
- Partner nations
- United KingdomChinaGermany
In The Last Decade
Tam Sobeih
4 papers receiving 326 citations
Tam Sobeih's Hit Papers
Peers
Comparison fields: 5 of 56
- Analytical Chemistry 107
- Plant Science 225
- Ecology 145
- Health Informatics 5
- Media Technology 29
Countries citing papers authored by Tam Sobeih
This map shows the geographic impact of Tam Sobeih'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 Tam Sobeih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tam Sobeih more than expected).
Fields of papers citing papers by Tam Sobeih
This network shows the impact of papers produced by Tam Sobeih. 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 Tam Sobeih. The network helps show where Tam Sobeih may publish in the future.
Co-authors
The 24 scholars most cited alongside Tam Sobeih, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images Hit paper breakdown → | 2019 | 276 |
| 2 | 2022 | 31 | |
| 3 | 2022 | 23 | |
| 4 | 2017 | 1 | |
| 5 | 2024 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2026 | 0 |
About Tam Sobeih
Tam Sobeih is a scholar working on Plant Science, Ecology, Analytical Chemistry, Environmental Engineering and Cognitive Neuroscience, having authored 7 papers that have together received 331 indexed citations. Recurring topics across this work include Smart Agriculture and AI (3 papers), Remote Sensing in Agriculture (2 papers), CCD and CMOS Imaging Sensors (1 paper), Spectroscopy and Chemometric Analyses (1 paper), Digital Imaging for Blood Diseases (1 paper), Data Stream Mining Techniques (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Analytical Chemistry (107 citations), Plant Science (225 citations), Ecology (145 citations), Health Informatics (5 citations) and Media Technology (29 citations). Tam Sobeih has collaborated with scholars based in United Kingdom, China and Germany. Frequent co-authors include Xin Zhang, Prof. Liangxiu Han, Lianghao Han, Yue Shi, Huiqin Ma, Pablo González‐Moreno, Yingying Dong, Huichun Ye, Wenjiang Huang and Mark A. Lee. Their work appears in journals such as Remote Sensing, Neurocomputing, IEEE Journal of Biomedical and Health Informatics, Lecture notes in computer science and Smart Agricultural Technology.
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.