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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. Figure from "Tackling Climate Change with Machine Learning."<ref name=":0" />]]
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>.
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== Background Readings ==
* [https://doi.org/10.1016/j.agsy.2020.103016 Machine learning for large-scale crop yield forecasting]
* ''[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]''
* [https://www.mdpi.com/2072-4292/11/6/676 Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review]
== Online Courses and Course Materials ==
* '''CGIAR-Platform for Big Data in Agriculture:''' A series of webinars for data management and data mining related to crop improvement and food security. Website [https://bigdata.cgiar.org/webinars/ here].
* '''Geocomputation with R:''' An open source book for geographic data analysis using R. Website [https://geocompr.robinlovelace.net/index.html here].
== Conferences, Journals, and Professional Organizations ==
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=== Journals ===
*'''Computers and Electronics in Agriculture:''' International journal covering computer hardware and software for solving problems in agriculture, agronomy and horticulture. Website [https://www.journals.elsevier.com/computers-and-electronics-in-agriculture
*'''Precision Agriculture:''' International Journal on Advances in Precision Agriculture. Website [https://www.springer.com/journal/11119
*'''Remote Sensing in Agriculture and Vegetation''': Open-access journal focusing on remote sensing with special issues related to agricultural applications. Website [https://www.mdpi.com/journal/remotesensing/sections/RSAV here].
=== Workshops ===
* '''ICLR 2020 Workshop on''' '''Computer Vision for Agriculture:''' Exposes the progress and unsolved problems of computational agriculture to the AI research community. Website [https://www.cv4gc.org/cv4a2020/ here].
* '''CVPR 2020/2021 Workshop and Prize Challenge on Agriculture-Vision:''' Present recent progress on computer vision research for tackling impactful challenges in agriculture. Website [https://www.agriculture-vision.com/ here].
=== Professional Organizations and Conferences ===
* '''ISPA:''' International Society of Precision Agriculture. Website [https://www.ispag.org/ here].
* '''ECPA 2021:''' European Conference on Precision Agriculture. Website [https://www.ecpa2021.hu/ here].
* '''ASABE:''' American Society of Agricultural and Biological Engineers. Website [https://www.asabe.org/ here].
* '''ASABE AIM 2021:''' American Society of Agricultural and Biological Engineers Annual International Meeting. Website [https://www.asabemeetings.org/ here].
* '''GRSS-IEEE:''' The IEEE Geoscience and Remote Sensing Society. Website [https://www.grss-ieee.org/ here].
* '''IGARSS 2021:''' International Geoscience and Remote Sensing Symposium. Website [https://igarss2021.com/ here].
* '''ICGIRSA 2021:''' International Conference on GIS and Remote Sensing in Agriculture. Website [https://waset.org/gis-and-remote-sensing-in-agriculture-conference-in-june-2021-in-copenhagen here].
* '''DAGM:''' The German Association for Pattern Recognition. Website [https://www.dagm.de/the-german-association-for-pattern-recognition here].
* '''DAGM GCPR 2021:''' DAGM German Conference on Pattern Recognition. Website [https://dagm-gcpr.de/ here].
* '''SPIE:''' The International Society for Optics and Photonics. Website [https://spie.org/about-spie?SSO=1 here].
* '''SPIE conference:''' SPIE conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. Website [https://spie.org/si21/conferencedetails/autonomous-sensing-systems-agricultural?utm_id=rsi21scpw&SSO=1 here].
* '''WorldAgri-Tech Innovation Summit 2021:''' International summit for agri-business networking. Website [https://worldagritechinnovation.com/ here].
=== Groups and Labs ===
* '''Australian Centre for Field Robotics:''' Robotic institute focusing on autonomous robots that can work in outdoor environment. Website [https://www.sydney.edu.au/engineering/our-research/robotics-and-intelligent-systems/australian-centre-for-field-robotics.html
▲* [https://www.sydney.edu.au/engineering/our-research/robotics-and-intelligent-systems/australian-centre-for-field-robotics.html Australian Centre for Field Robotics]
== Library and Tools ==
Some packages for working with remote sensing data are,
*
* '''RSCD:''' A MATLAB toolbox for remote sensing change detection. [https://github.com/Bobholamovic/ChangeDetectionToolbox Github].
* '''geemap:''' A Python package for interactive mapping with Google Earth Engine. [https://github.com/giswqs/geemap/tree/master/examples Github], [https://geemap.org/ website].
* A list of Python and R codes and different resources for geospatial analysis and EO data. [https://github.com/acgeospatial/awesome-earthobservation-code Github].
* An updated list of geospatial analysis tools. [https://github.com/sacridini/Awesome-Geospatial Github].
== Data ==
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=== Remote Crop Identification ===
*
* LandCoverNet: A multispectral satellite imagery dataset acquired from Sentinel-2 and can be used for land cover classification. The dataset was created by the Radiant Earth Foundation. [http://registry.mlhub.earth/10.34911/rdnt.d2ce8i/ Dataset] and [https://radiant-mlhub.s3-us-west-2.amazonaws.com/landcovernet/Documentation.pdf documentation].
* '''CropHarvest''': The [https://openreview.net/forum?id=JtjzUXPEaCu CropHarvest dataset], compiled by researchers affiliated with NASA Harvest, provides a unified global dataset, API, and benchmarks for ML methods in crop-type mapping.
=== Kaggle datasets ===
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