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[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
Agriculture is responsible for about 14% of GHG emissions
Such industrial agriculture approaches are ultimately based on making farmland more uniform and predictable. This allows it to be managed at scale using basic automation tools like tractors, but can be both more destructive and less productive than approaches that work with the natural heterogeneity of land and crops. Increasingly, there is demand for sophisticated tools which would allow farmers to work at scale, but adapt to what the land needs. This approach is often known as “precision agriculture.”
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[[Remote Sensing Datasets]] offers great opportunity to monitor agriculture and can be georeferenced to match ground measurements. In particular, [https://earthengine.google.com/ Google earth engine] offers a convenient interface over freely available satellite imagery such as [https://developers.google.com/earth-engine/datasets/catalog/landsat/ Landsat] and [https://developers.google.com/earth-engine/datasets/catalog/sentinel/ Sentinel].
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* [https://arxiv.org/abs/2001.01306 Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis]
=== Kaggle datasets ===
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