Automating Crop Canopy Data Collection for Crop Management: Difference between revisions

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(Created page with "Contact: Michael Gifford Michael.Gifford@niab.com Collecting data from farmers relating to crop diseases and pests is routine. What has yet to be effectively developed is a...")
 
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Contact: Michael Gifford Michael.Gifford@niab.com
Contact: Michael Gifford, [[NIAB]] <Michael.Gifford@niab.com>


Collecting data from farmers relating to crop diseases and pests is routine. What has yet to be effectively developed is a real-time system for farmers to report problems as they find them in the field.  The barriers to doing this are the ease of use of any application and concerns around how the data will be used once reported.
 
The benefits to the industry of accurate, timely reporting are significant.  There is the potential for a significant reduction in crop losses, a reduction in the use of crop protection chemicals and a greater understanding of how crop diseases and pests develop and spread geographically and temporally.
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.
The challenge is to develop a mobile reporting app with an intuitive and rapid user interface. You will need to develop an understanding of what interest and incentive farmers have to report issues and the barriers that prevent this reporting.

Latest 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.

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