Sugar beet phenotyping in breeding trials using UAV (BEETPHEN)

Start-End 01/12/2016 -  28/02/2020
Programme STEREO 3
Contract SR/00/346
Objective Sugarbeet field phenotyping with high accuracy and reliability for assessment of resistant sugarbeet to biotic stresses is required but lacking. The small dimensions of breeding trial plots and the need for frequent revisits hamper the use of airborne images, especially with UAVs, able to deliver assessments at a very high spatial resolution pixel size with a very flexible temporal resolution. High-resolution hyperspectral remote sensing imagery is described to be appropriate to produce optical indices related to the crops health status. This project aims at applying the UAVs potential for the quantitative assessment of a specific plant trait (disease resistance against Powdery Mildew ‘PM’) within breeding trial plots.
Method Disease symptoms will be assessed during two successive years through several specific artificially inoculated breeding trials. Inoculated trials will be compared to non-inoculated ones in a replicated plots experimental design. Selected varieties will show a gradient of sensitivity to PM. In situ visual observations (disease symptoms quantification) will be performed several times after crop inoculation. In the meantime UAV images (with embedded RGB, multispectral and hyperspectral imaging sensors) and similar ground-based spectral information will be acquired.
Result Expected Scientific Results:

Evaluation of different sensors and platforms combinations and selection of the most suitable one for PM detection and quantification. Development of a data acquisition and processing methodology for the assessment of the resistance to PM for sugar beet breeding trials using UAV imaging, and validation of the produced algorithm.

Expected Products and Services:

This project is expected to deliver a rapid operator-independent tool to assess in a standardized way diseases at the breeding trial plot level.
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Team
Project Leader: AMAND Olivier SESVanderHave
Team Member: GOFFART Jean-Pierre CRA-W (Centre wallon de Recherches agronomiques)
Team Member: DELALIEUX Stephanie VITO (Vlaamse Instelling voor Technologisch Onderzoek)
Location
Sensors used
Applications Agriculture
Cartography
Geology & soil
Hyperspectral
Land planning & infrastructures
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