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Agriculture (edit)

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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 [26]<ref>{{Cite book|last=Intergovernmental Panel on Climate Change|url=http://ebooks.cambridge.org/ref/id/CBO9781107415416|title=Climate Change 2014 Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change|date=2014|publisher=Cambridge University Press|isbn=978-1-107-41541-6|location=Cambridge|doi=10.1017/cbo9781107415416}}</ref>. This might come as a surprise, since plants take up CO<sub>2</sub> from the air. However, modern industrial agriculture involves more than just growing plants. First, the land is stripped of trees, releasing carbon sequestered there. Second, the process of tilling exposes topsoil to the air, thereby releasing carbon that had been bound in soil aggregates and disrupting organisms in the soil that contribute to sequestration. Finally, because such farming practices strip soil of nutrients, nitrogen-based fertilizers must be added back to the system. Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption [386]. Moreover, while some of this nitrogen is absorbed by plants or retained in the soil, some is converted to nitrous oxide, a greenhouse gas that is about 300 times more potent than CO<sub>2</sub>.
 
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].
 
=== SoilRemote measurementsCrop Identification ===
 
* [https://arxiv.org/abs/2001.01306 Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis]
 
=== Kaggle datasets ===
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