Project / Initiative
Contracts and agreements
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Inactive
AgriUAV
Estimation of Agricultural variables from UAV data
Participant
Participant
AgriUAV is a project devoted to the estimation of agricultural variables from Unmanned Aerial Vehicles (UAV) data. UAV technology opens new horizons in high precision agriculture for effective characterization of variability in cropland state variables at high spatial resolution and high revisit frequency.
Among the canopy state variables accessible from remote sensing data, Leaf Area Index (LAI) and Chlorophyll content (Cab) have raised special interest for mapping crop status and monitoring its time-course. LAI is a key variable for characterizing green biomass and assessing crop nitrogen needs. Leaf (Cab) and canopy level chlorophyll content variable, Cab×LAI, are good indicator of photosynthesis activity, stress and nutritional state. In AgriUAV project, we aim to develop UAV technologies and retrieval algorithms for monitoring LAI and Cab variables to support precision agriculture and high-throughput phenotyping. This research is framed into the CREAF-CEAB-CSIC-UAB Global Ecology Unit research on remote sensing. AgriUAV is a public-private technology transfer project lead by CREAF in collaboration with INRA (Institut National de la Recherche Agronomique) and Airinov company. The main research objectives are:
- Support the design of optical sensors on board UAV instruments for optimal LAI and Cab monitoring
- Develop inversion techniques of radiative transfer models for the derivation of LAI and Cab over wheat, corn and rapeseed crops.
- Validate the accuracy and stability of estimates to ensure their reliability and temporal repeatability for high precision agriculture and high-throughput phenotyping.
