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Topic 3D: Part 1 - AI for Agriculture: Precision Farming

Featured Images and Example Data
Crop identification using optical Sentinel-2 time series. The top image (a) shows the mature growth stage of irrigated maize fields in red, and the early growth stage of flooded rice in brown. The middle image (b) shows developed rice in red areas.
Crop identification using optical Sentinel-2
Crop identification using optical Sentinel-2 time series. The top image (a) shows the mature growth stage of irrigated maize fields in red, and the early growth stage of flooded rice in brown. The middle image (b) shows developed rice in red areas.

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Sylvain Ferrant, et al., 2017
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ESA BIC Hessen & Baden Württemberg start-up Spacenus improves precision farming with their Artificial Intelligence (AI) augmented system for automating field geometrics, plant nutrient detection and more.
Spacenus Precision Farming
ESA BIC Hessen & Baden Württemberg start-up Spacenus improves precision farming with their Artificial Intelligence (AI) augmented system for automating field geometrics, plant nutrient detection and more.

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Spacenus
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This is an example of crop-type mapping using Sentinel-1 satellite imagery
Crop-type map
This is an example of crop-type mapping using Sentinel-1 satellite imagery

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Ground truth data: Regional Government of Andalusia. Crop map: contains modified Copernicus Sentinel data (2017), processed by University of Alicante
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This animation shows early season crop type classification in the Emmeloord region of the Netherlands in June 2018 based on Copernicus Sentinel-2 data. Green shows summer crops, red: potatoes, orange: vegetables and flowers, yellow: cereals, and blue: grass.
Crops in the Netherlands
This animation shows early season crop type classification in the Emmeloord region of the Netherlands in June 2018 based on Copernicus Sentinel-2 data. Green shows summer crops, red: potatoes, orange: vegetables and flowers, yellow: cereals, and blue: grass.

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contains modified Copernicus Sentinel data (2018), processed by ESA/GeoVille
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