Étienne David
Impact in
- Analytical Chemistry top 10%
- Spectroscopy and Chemometric Analyses
- Plant Science top 10%
- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
Papers in
- Ecology 5
- Remote Sensing in Agriculture 5
-
- Smart Agriculture and AI 5
- Co-authors
- Frédéric Baret (5 shared papers)Simon Madec (4 shared papers)Benoît de Solan (3 shared papers)Scott Chapman (4 shared papers)Wei Guo (4 shared papers)Ian Stavness (2 shared papers)Curtis Pozniak (1 shared paper)Pouria Sadeghi‐Tehran (1 shared paper)
- Journals
- Plant Phenomics (4 papers)Scientific Data (1 paper)IEEE Transactions on Information Theory (1 paper)IRIS Research product catalog (Sapienza University of Rome) (1 paper)International Journal For Multidisciplinary Research (1 paper)
In The Last Decade
Étienne David
8 papers receiving 228 citations
Peers
Comparison fields: 5 of 45
- Analytical Chemistry 61
- Plant Science 187
- Ecology 109
- Ecological Modeling 11
- Environmental Engineering 32
Countries citing papers authored by Étienne David
This map shows the geographic impact of Étienne David'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 Étienne David with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Étienne David more than expected).
Fields of papers citing papers by Étienne David
This network shows the impact of papers produced by Étienne David. 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 Étienne David. The network helps show where Étienne David may publish in the future.
Co-authors
The 25 scholars most cited alongside Étienne David, 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 | 2020 | 168 | |
| 2 | 2022 | 29 | |
| 3 | 2023 | 20 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 4 | |
| 6 | 2025 | 2 | |
| 7 | 2023 | 2 | |
| 8 | 1962 | 1 |
About Étienne David
Étienne David is a scholar working on Ecology, Plant Science, Analytical Chemistry, Computer Vision and Pattern Recognition and Ecological Modeling, having authored 8 papers that have together received 234 indexed citations. Recurring topics across this work include Remote Sensing in Agriculture (5 papers), Smart Agriculture and AI (5 papers), Spectroscopy and Chemometric Analyses (2 papers), Species Distribution and Climate Change (1 paper), Remote-Sensing Image Classification (1 paper), Digital Imaging for Blood Diseases (1 paper), Remote Sensing and LiDAR Applications (1 paper) and Industrial Vision Systems and Defect Detection (1 paper). The work is most often cited by research in Analytical Chemistry (61 citations), Plant Science (187 citations), Ecology (109 citations), Ecological Modeling (11 citations) and Environmental Engineering (32 citations). Étienne David has collaborated with scholars based in France, Australia and Japan. Frequent co-authors include Frédéric Baret, Simon Madec, Benoît de Solan, Scott Chapman, Wei Guo, Ian Stavness, Curtis Pozniak, Pouria Sadeghi‐Tehran, Norbert Kirchgeßner and Helge Aasen. Their work appears in journals such as Plant Phenomics, Scientific Data, IEEE Transactions on Information Theory, IRIS Research product catalog (Sapienza University of Rome) and International Journal For Multidisciplinary Research.
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.