Crop Type Classification Using Satellite Imagery
We can detect crop types historically back to 2016 as well as in the growth season, approx. 31-37 on Zadoks scale depending on the region and crop-type. In a Norwegian context we start run the model in the middle of July and achieve our best results in the middle of August, approximately 1.5 months prior to harvesting.
We’re always working hard to bring new regions like France, Spain, Brazil, US, Australia, Germany and 30+ more.
We've prepared a convenient system of discounts for increasing the volume of use. More requests - less price.
We provide full access to all our documentation and simple API / Add-ons endpoints for easy integration.
How do we classify all crops?
Crop Classification Model built on the baseline of crop specific yield-data. The alpha version we released in 2019 achieved a 83-87% accuracy based on single-pixel algorithm (Sentinel-1), while our latest model released we reached an accuracy of 92% based on object-based Sentinel-2 algorithm.