Alexis Comar
researcher ORCID ID = 0000-0002-6919-0469
Facts
8 properties- Instance of
- human
- Date of birth
- 1986
- Occupation
- researcher
- Employer
- ARVALIS - Institut du végétal (2009–2013)[3]
- Doctoral advisor
- Frédéric Baret[4]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Alexis Comar
🔖 Identifiers · 6
- ORCID iD
- 0000-0002-6919-0469
- VIAF cluster ID
- 313565360
- OpenAlex ID
- A5011582896as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- Dimensions author ID
- 010352145117.25
- IdRef ID
- 178216941
- Scopus author ID
- 55053583500
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 7
An automatic method based on daily in situ images and deep learning to date wheat heading stage,
Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites,
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution,
Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery,
Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery,
Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops,
Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems
← Authored by 7
An automatic method based on daily in situ images and deep learning to date wheat heading stage,
Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites,
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution,
Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery,
Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery,
Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops,
Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems
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