Agriculture: Difference between revisions
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== Machine Learning Application Areas ==
*[[Precision agriculture|'''Precision agriculture''']]: Typical industrial agriculture releases CO<sub>2</sub> 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|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.
* [[Greenhouse Gas Emissions Detection|'''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 ==
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