Automating Crop Canopy Data Collection for Crop Management: Difference between revisions
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Models to optimise potato crop production forecast yield and schedule irrigation | Models to optimise potato crop production forecast yield and schedule irrigation use manually collected data on leaf canopy coverage to quantify light interception and evapotranspiration -- time-consuming, expensive and often inaccurate. Such data can be collected by satellite but optical sensing is impeded by cloud cover in Northern Europe. Synthetic aperture radar (SAR) imagery from the Copernicus Programme is collected through clouds and during the night but there is no available service for estimating canopy cover from SAR imagery. Your challenge is to develop a machine learning system to estimate canopy cover from SAR imagery and integrate with existing models. | ||
Revision as of 21:50, 19 November 2019
Contact: Michael Gifford, NIAB <Michael.Gifford@niab.com>
Models to optimise potato crop production forecast yield and schedule irrigation use manually collected data on leaf canopy coverage to quantify light interception and evapotranspiration -- time-consuming, expensive and often inaccurate. Such data can be collected by satellite but optical sensing is impeded by cloud cover in Northern Europe. Synthetic aperture radar (SAR) imagery from the Copernicus Programme is collected through clouds and during the night but there is no available service for estimating canopy cover from SAR imagery. Your challenge is to develop a machine learning system to estimate canopy cover from SAR imagery and integrate with existing models.
