![We can now detected fields and crops via satellite - Crops in the Netherlands - green shows summer crops, red is potatoes, orange is market crops, yellow is cereals and blue depicts grassland
[[MORE]] The image shows different crop types around...](https://64.media.tumblr.com/41ccfa506f32b36cb3318ae99879599c/tumblr_pvnf0fRlgA1rasnq9o1_1280.jpg)
We can now detected fields and crops via satellite - Crops in the Netherlands - green shows summer crops, red is potatoes, orange is market crops, yellow is cereals and blue depicts grassland
The image shows different crop types around Emmeloord in the Netherlands. Here, green shows summer crops, red is potatoes, orange is market crops, yellow is cereals and blue depicts grassland. The area is important for the agrofood sector and, in particular, has strong ties to the international potato industry. By integrating Copernicus Sentinel-2 based crop-type monitoring directly into existing industry workflows, the agrofood industry can gain information about the growth and potential yield of crops, potatoes in particular, including the impact of ongoing droughts.
First interactive map with AI detected fields and crops
This unique interactive map allows you to explore and compare fields and crops in Europe and the USA. Zoom in and get to know the field: the hectarage, the crop, and the field score. In addition, on the chart, you can see how the field has changed over the past three years. Zoom out and understand the world: fields sizes and crops are displayed for each region. Compare ratings and get insights for more than 40 countries on the desktop and mobile devices.
How does it work?
The map was created by a Belarus-based ag-tech startup OneSoil. We build technologies that analyze satellite imagery using machine learning algorithms. It took us half a year to create the map. First, we learned how to clean the satellite photos from artifacts to ensure correct processing of information. Second, we trained an algorithm to allocate field boundaries automatically. For the map, we simplified the boundaries so that the visualization is really fast. The accuracy of crop classification, or F1 score, is 0.91. Third, we trained another algorithm to automatically determine a crop that grows on a field. Fourth, we created what you can now see: the map.
Credits: European Union, contains Copernicus Sentinel data (2016-2017-2018).
(Source: esa.int)















