Predicting crop yields with satellite imagery
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Predicting crop yields with satellite imagery

Succeeding in global commodity markets, requires robust data that gives you an informational edge. But all too often, the available data is patchy and unreliable. Parallel partnered with HSAT – a leading machine learning company – to design and build a visual analytics platform that aims to increase coverage and reduce uncertainty, using automated analysis of satellite imagery and ground level photography.

In order to provide their clients with meaningful insights, HSAT are using powerful machine learning algorithms on vast amounts of raw data sourced from satellites such as the European Space Agency's Sentinel-2.

In addition to the raw data being hard to work with, the standard applications typically used to analyse this type of data are complicated tools that require academic-level expertise. Given that HSAT's clients operate in a fast paced environment and are not domain experts in geospatial analysis, it was vital that the Inference platform delivered insights that were focused, timely and trusted – with the ability to validate findings using additional sources of data.

Our approach - sweat the important stuff

Our primary design principle in response to this challenge was to focus on the key metrics and functionality that commodity traders need - and to strip out everything else to reduce the time-to-insight ratio and provide bespoke and highly-processed data points.

For someone looking for commodities intelligence in this sector, the most important metrics are the area of land given over to sugar cultivation, levels of precipitation, yield and price over time.

The application was design to provide two main views on this data. The first showing historical data on sugarcane volumes (hectares) and precipitation (highly correlated to yield).


Analysts typically want to understand patterns at a macro level - the expected volumes and yields for entire regions and provinces. The second view of the application provides this through heat-map visualisations that instantly direct the viewer to the provinces with the highest amount of sugar crops as well as the biggest changes in sugar across a number of years.

Reducing uncertainty

It was also important that users were able to trust the data. As with most new technologies, it is prudent to maintain a level of oversight and understanding about what machine learning algorithms are seeing and interpreting.

HSAT's approach to this is to periodically enhance their satellite imagery datasets with the ground level imagery. This is done by sending out a mix of scouts and drones that will probe the crop lands with both video footage and photographic images.


This approach has two benefits: not only does it help human analysts to validate the data that they are seeing on the platform - via editorial style reports - but it also serves as a feedback loop for the detection algorithms, which improves their accuracy over time.

Platform strategy

Additional components include Field Viewer to help users assess the likelihood of crops suffering from viruses and Contract Manager to provide detailed information of the crops being grown and expected yields for specific farms within a portfolio.

Insight from above

Making the most of today's advanced analytics techniques requires intuitive, domain specific applications. If you have a project you'd like to discuss, please get in touch

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