National Level Reporting. Field Level Accuracy. HSAT can provide analysis on crop health, yield, irrigation, and sustainability.

Increasing –  populations, sustainable practices and extreme weather events – means an in ever-increasing demands for farmers.  HSAT can help tackle these challenges by providing accurate, reliable, and efficient information.

HSAT can provide reports on a global scale or a field level. This is possible because the analysis is conducted at 10x10m level, and then expanded to cover a farm, field, region or country.

HSAT works to understand your challenge and then build a bespoke solution to address those needs – from understanding crop production and transport logistics to identifying areas of crop disease or requiring irrigation for precision agriculture.

HSAT is supporting farmers, working in three different continents. Improving existing systems and well as providing new capabilities. 


Examples of HSAT supporting farmers

  • Mapping crop areas
  • Understanding crop health and yield
  • Supporting precision agriculture
  • Logistics – mapping crops and farms to silos, mills and plants


HSAT can offer these capabilities as a managed service, with the provision of dashboards and alert systems to indicate potential risks. 



Related Articles

The below articles provide examples of how alternative data – satellites, drones and weather – can be used to support farmers.  Working at a global scale with field-level accuracy.


Case Study: Crop Disease and Irrigation

A client needed to monitor crops for risk of disease and drought/poor irrigation on a national scale; the client needed weekly updates on the risks to crops and production.  The scale of the fields and the land overall meant it was not possible to check all of the fields every day manually. Even with drones, it would have been cost-prohibitive, costing tens of millions.  For this reason, we built a solution that looked at every 10m of land every week, across the entire country and used machine learning, based on billions of data points, to detect disease.
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Predicting Crops and Crop Yields

Predicting crops and crop yields has been tried for centuries. A hundred years ago regression models were used. Now satellite data, combined with machine learning, is used to predict crops. It's only very recently becoming accurate.
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Farming Practices – ESG and Data Challenges

Farming is often criticized for its environmental credentials – it consumes over 70% of the entire worlds freshwater supply, over 30% of the Earth’s land is taken up for crops - however – agriculture feeds over 7 billion people and employs over 30% of the entire global workforce – it is essential to human life.
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