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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]. 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.”
== Data ==▼
== Problem Areas ==
===
Some packages for working with remote sensing data are,▼
* Weed and pest detection
* [https://medium.com/sentinel-hub/introducing-eo-learn-ab37f2869f5c eo-learn]: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing▼
* Soil sensing
* Farming assistant
===
Development of autonomous robots for performing specific tasks on crops.
=== Monitoring Agriculture ===
* ''[https://link.springer.com/article/10.1007/s11069-017-3106-x Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices]''▼
* Estimating carbon in soil and soil health
* Estimating emissions of methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O)
* Segmentation and classification of crops
* Quantifying cattles and other farm animals
== Background Readings ==
▲*
*[https://www.mdpi.com/2072-4292/11/6/676 Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review]
== Online Courses and Course Materials ==
<br />
== Community ==
=== Journals
*[https://www.journals.elsevier.com/computers-and-electronics-in-agriculture Computers and Electronics in Agriculture]
*[https://www.springer.com/journal/11119 Precision Agriculture]
▲Some packages for working with remote sensing data are,
▲* [https://medium.com/sentinel-hub/introducing-eo-learn-ab37f2869f5c eo-learn]: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing
▲== Data ==
===
[[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].
===
== References ==
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