Jérôme Théau

1.8k citations
46 papers · 1.3k · 1 hit paper · h-index 18

Impact in

Papers in

Jérôme Théau

46 papers receiving 1.3k citations

Jérôme Théau's Hit Papers

Convolutional Neural Networks for the Automatic Identification of Plant Diseases 2019 · 258 citations
2580+2+4Years since publication50100150200250

Peers

Jérôme Théau
Comparison fields: 5 of 86
  • Geology 189
  • Ecological Modeling 140
  • Environmental Engineering 359
  • Space and Planetary Science 28
  • Ecology 547
Replace Roope Näsi with:
Roope Näsi Finland
Jere Kaivosoja Finland
Niko Viljanen Finland
Grant D. Pearse New Zealand
Lauri Markelin Finland
Andrea Berton Italy
Jonáš Hruška Portugal
Jessica J. Mitchell United States
Rachel Gaulton United Kingdom
Jan van Aardt United States
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Citations per field
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Citations per year

Countries citing papers authored by Jérôme Théau

Since Specialization
Citations

This map shows the geographic impact of Jérôme Théau'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 Jérôme Théau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jérôme Théau more than expected).

Fields of papers citing papers by Jérôme Théau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jérôme Théau. 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 Jérôme Théau. The network helps show where Jérôme Théau may publish in the future.

Co-authors

The 25 scholars most cited alongside Jérôme Théau, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jérôme Théau Line = papers co-authored together Jérôme Théau links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Convolutional Neural Networks for the Automatic Identification of Plant Diseases
Hit paper breakdown →
2019258
2 2014152
3 2016130
4 2015109
5 202062
6 202161
7 201559
8 200441
9 201537
10 201535
11 202134
12 201528
13 202027
14 202327
15 201926
16 200726
17 201925
18 202019
19 202317
20 200416

About Jérôme Théau

Jérôme Théau is a scholar working on Ecology, Global and Planetary Change, Ecological Modeling, Computer Vision and Pattern Recognition and Environmental Engineering, having authored 46 papers that have together received 1.3k indexed citations. Recurring topics across this work include Wildlife Ecology and Conservation (13 papers), Land Use and Ecosystem Services (10 papers), Species Distribution and Climate Change (9 papers), Remote Sensing in Agriculture (8 papers), Remote Sensing and LiDAR Applications (8 papers), Ecology and Vegetation Dynamics Studies (7 papers), Robotics and Sensor-Based Localization (6 papers) and Advanced Vision and Imaging (5 papers). The work is most often cited by research in Geology (189 citations), Ecological Modeling (140 citations), Environmental Engineering (359 citations), Space and Planetary Science (28 citations) and Ecology (547 citations). Jérôme Théau has collaborated with scholars based in Canada, United States and Belgium. Frequent co-authors include Patrick Ménard, Mozhdeh Shahbazi, Samuel Foucher, Pierre-Luc St-Charles, Gunho Sohn, Xin Wen, Richard Fournier, Claude Duguay, Lydiane Aubé and Nicolas Devillers. Their work appears in journals such as GIScience & Remote Sensing, PLoS ONE, Remote Sensing of Environment, ISPRS Journal of Photogrammetry and Remote Sensing and Frontiers in Ecology and Evolution.

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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