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This page is about the intersection of agriculture and machine learning in the context of climate change mitigation. For an overview of agriculture as a whole, please see the Wikipedia page on this topic.

A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use. Figure from "Tackling Climate Change with Machine Learning."^[1]

As described in the paper "Tackling Climate Change with Machine Learning"^[1]:

Agriculture is responsible for about 14% of GHG emissions^[2]. This might come as a surprise, since plants take up CO₂ 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^[3]. 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 CO₂.
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.”

Machine Learning Application Areas

Precision agriculture: Typical industrial agriculture releases CO₂ into the atmosphere by disrupting natural soil chemistry and biodiversity, and also requires chemicals that are emissions-intensive both to produce and to use. ML can help monitor emissions; reduce the need for chemicals by pinpointing pests, diseases, and weeds; and change agricultural paradigms by controlling physical robots.
Food security: By affecting rainfall and the timing of growing seasons, climate change poses a risk to food security. Machine learning can support information gathering around food supply chains, providing early warnings about -- and triggering preventative action around -- famines.
Monitoring agricultural emissions: Agriculture is a major contributor to greenhouse gas emissions via methane (in particular from cattle farming) and nitrous oxide (from fertilizer), in addition to carbon dioxide from soil carbon breakdown. Machine learning can help track agricultural emissions directly, in addition to monitoring crop cover and livestock to help assess emissions potential.

Background Readings

Online Courses and Course Materials

Conferences, Journals, and Professional Organizations

Journals

Workshops

Computer Vision for Agriculture, ICLR 2020

Groups and Labs

Library and Tools

Some packages for working with remote sensing data are,

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

Remote Sensing Datasets offers great opportunity to monitor agriculture and can be georeferenced to match ground measurements. In particular, Google earth engine offers a convenient interface over freely available satellite imagery such as Landsat and Sentinel.

Remote Crop Identification

Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis

Kaggle datasets

References

↑ ^1.0 ^1.1 Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
↑ Intergovernmental Panel on Climate Change (2014). Climate Change 2014 Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. doi:10.1017/cbo9781107415416. ISBN 978-1-107-41541-6.
↑ Montoya, Joseph H.; Tsai, Charlie; Vojvodic, Aleksandra; Nørskov, Jens K. (2015-06-10). "The Challenge of Electrochemical Ammonia Synthesis: A New Perspective on the Role of Nitrogen Scaling Relations". ChemSusChem. 8 (13): 2180–2186. doi:10.1002/cssc.201500322. ISSN 1864-5631.

[:0-1] 1.0 ^1.1 Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].

[2] Intergovernmental Panel on Climate Change (2014). Climate Change 2014 Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. doi:10.1017/cbo9781107415416. ISBN 978-1-107-41541-6.

[3] Montoya, Joseph H.; Tsai, Charlie; Vojvodic, Aleksandra; Nørskov, Jens K. (2015-06-10). "The Challenge of Electrochemical Ammonia Synthesis: A New Perspective on the Role of Nitrogen Scaling Relations". ChemSusChem. 8 (13): 2180–2186. doi:10.1002/cssc.201500322. ISSN 1864-5631.

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-''This page is about the intersection of agriculture and machine learning in the context of climate change mitigation. For an overview of agriculture as a whole, please see the [https://en.wikipedia.org/wiki/Agriculture Wikipedia page] on this topic.''[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
+''This page is about the intersection of agriculture and machine learning in the context of climate change mitigation. For an overview of agriculture as a whole, please see the [https://en.wikipedia.org/wiki/Agriculture Wikipedia page] on this topic.''[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use. Figure from "Tackling Climate Change with Machine Learning."<ref name=":0" />]]
-As described in the paper "Tackling Climate Change with Machine Learning"<ref>{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>:<blockquote>
+As described in the paper "Tackling Climate Change with Machine Learning"<ref name=":0">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>:<blockquote>
 Agriculture is responsible for about 14% of GHG emissions<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<ref>{{Cite journal|last=Montoya|first=Joseph H.|last2=Tsai|first2=Charlie|last3=Vojvodic|first3=Aleksandra|last4=Nørskov|first4=Jens K.|date=2015-06-10|title=The Challenge of Electrochemical Ammonia Synthesis: A New Perspective on the Role of Nitrogen Scaling Relations|url=http://dx.doi.org/10.1002/cssc.201500322|journal=ChemSusChem|volume=8|issue=13|pages=2180–2186|doi=10.1002/cssc.201500322|issn=1864-5631}}</ref>. 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>.