Agriculture

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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.

Agriculture is responsible for about 14% of GHG emissions[1]. 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[2]. 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[edit | edit source]

Precision agriculture[edit | edit source]

This can increase the long term yield, reduce the need for tillage and fertilizers.

  • Weed and pest detection
  • Soil sensing
  • Farming assistant
  • Autonomous farming

Monitoring agriculture[edit | edit source]

This helps studying trends and encourage good practices

  • Remote sensing of soil composition
  • Estimating emissions of methane (CH4) and nitrous oxide (N2O)
  • Segmentation and classification of crops
  • Quantifying cattles and other farm animals

Background Readings[edit | edit source]

Online Courses and Course Materials[edit | edit source]

Community[edit | edit source]

Journals[edit | edit source]

Workshops[edit | edit source]

Groups and Labs[edit | edit source]

Library and Tools[edit | edit source]

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[edit | edit source]

Remote sensing[edit | edit source]

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[edit | edit source]

Kaggle datasets[edit | edit source]

References[edit | edit source]

  1. 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.
  2. 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.