Editing Precision agriculture (section)

==Principles==
Precision agriculture uses many tools, but some of the basics include tractors, combines, sprayers, planters, and diggers, which are all considered auto-guidance systems. The small devices on the equipment that use [[Geographic information system|GIS (geographic information system)]] are what makes precision agriculture what it is; the GIS system can be thought of as the “brain”. To be able to use precision agriculture, the equipment needs to be wired with the right technology and data systems. More tools include [[Variable rate application|Variable rate technology (VRT)]], [[Global positioning system]], [[Geographical information system]], [[Environmental monitoring|Grid sampling]], and remote sensors.<ref>{{Cite web|url=https://precisionagricultu.re/important-tools-to-succeed-in-precision-farming/|title=Important tools to succeed in precision farming|language=en-US|access-date=20 November 2019|archive-date=31 October 2019|archive-url=https://web.archive.org/web/20191031153041/http://precisionagricultu.re/important-tools-to-succeed-in-precision-farming/|url-status=dead}}</ref>

===Geolocating===
[[Geolocating]] a field enables the farmer to overlay information gathered from the analysis of soils and residual nitrogen, and information on previous crops and soil resistivity. Geolocation is done in two ways
* The field is delineated using an in-vehicle GPS receiver as the farmer drives a tractor around the field.
* The field is delineated on a basemap derived from aerial or satellite imagery. The base images must have the right level of resolution and geometric quality to ensure that geolocation is sufficiently accurate.

===Variables ===
Intra and inter-field variability may result from a number of factors. These include climatic conditions ([[hail]], drought, rain, etc.), [[soils]] (texture, depth, nitrogen levels), cropping practices ([[no-till farming]]), [[weed]]s, and disease.
Permanent indicators—chiefly soil indicators—provide farmers with information about the main environmental constants.
Point indicators allow them to track a crop's status, i.e., to see whether diseases are developing, if the crop is suffering from [[moisture stress|water stress]], nitrogen stress, or lodging, whether it has been damaged by ice, and so on.
This information may come from weather stations and other sensors (soil electrical resistivity, detection with the naked eye, satellite imagery, etc.).
[[Soil resistivity]] measurements combined with soil analysis make it possible to measure [[Water content|moisture content]]. Soil resistivity is also a relatively simple and cheap measurement.<ref>{{Cite web|url=https://pubs.ext.vt.edu/442/442-508/442-508_pdf.pdf|title=Precision Farming Tools: Soil Electrical Conductivity|access-date=12 June 2016}}</ref>

=== Strategies ===
[[File:SUAS StardustII Ndvi sml.jpg|thumb|[[NDVI]] image taken with small aerial system Stardust II in one flight (299 images mosaic)]]
Using [[soil map]]s, farmers can pursue two strategies to adjust field inputs:
* Predictive approach: based on analysis of static indicators (soil, [[resistivity]], field history, etc.) during the [[crop cycle]].
* Control approach: information from static indicators is regularly updated during the crop cycle by:
** sampling: weighing [[biomass]], measuring leaf [[chlorophyll]] content, weighing fruit, etc.
** remote sensing: measuring parameters like temperature (air/[[soil temperature|soil]]), humidity (air/[[soil humidity|soil]]/leaf), wind or stem diameter is possible thanks to [[Wireless Sensor Networks]]<ref>{{cite web|url=http://www.libelium.com/libeliumworld/articles/101651651444|title=New Waspmote Sensor Board enables extreme precision agriculture in vineyards and greenhouses- Libelium|website=www.libelium.com}}</ref> and [[Internet of things]] (IoT)
** proxy-detection: in-vehicle sensors measure leaf status; this requires the farmer to drive around the entire field.
** aerial or satellite remote sensing: [[Hyperspectral imaging|multispectral imagery]] is acquired and processed to derive maps of crop biophysical parameters, including indicators of disease.<ref>{{Cite journal|last=Mahlein|first=Anne-Katrin|date=1 September 2015|title=Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping|journal=Plant Disease|volume=100|issue=2|pages=241–251|doi=10.1094/PDIS-03-15-0340-FE|pmid=30694129|issn=0191-2917|doi-access=free}}</ref> Airborne instruments are able to measure the amount of plant cover and to distinguish between crops and weeds.<ref>{{Cite news|url=https://www.economist.com/technology-quarterly/2016-06-09/factory-fresh|title=The future of agriculture: Factory fresh |date=9 June 2016 |newspaper=The Economist|access-date=12 June 2016}}</ref>
Decisions may be based on decision-support [[Conceptual model|model]]s (crop simulation models and [[recommender system|recommendation]] models) based on [[big data]], but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the [[Natural environment|environment]]- a role being taken over by [[artificial intelligence]] (AI) systems based on [[machine learning]] and [[artificial neural networks]].

It is important to realize why PA technology is or is not adopted, "for PA technology adoption to occur the farmer has to perceive the technology as useful and easy to use. It might be insufficient to have positive outside data on the economic benefits of PA technology as perceptions of farmers have to reflect these economic considerations."<ref>{{Cite journal|last=Aubert|first=Benoit|date=2012|title=IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology|journal=Decision Support Systems|volume=54|pages=510–520|doi=10.1016/j.dss.2012.07.002|s2cid=9124615|url=https://publications.aston.ac.uk/id/eprint/40902/1/IT_as_enabler_of_sustainable_farming.pdf|access-date=26 November 2020|archive-date=8 May 2020|archive-url=https://web.archive.org/web/20200508100731/https://publications.aston.ac.uk/id/eprint/40902/1/IT_as_enabler_of_sustainable_farming.pdf|url-status=dead}}</ref>

===Implementing practices ===
New information and communication technologies make field-level crop management more operational and easier to achieve for farmers.
Application of crop management decisions calls for agricultural equipment that supports variable-rate technology ([[Variable Rate Technology|VRT]]), for example varying [[seed]] density along with the variable-rate application (VRA) of [[nitrogen]] and phytosanitary products.<ref name="nasa-feature">
{{cite web
|url=http://earthobservatory.nasa.gov/Features/PrecisionFarming/
|title=Precision Farming : Feature Articles
|publisher=earthobservatory.nasa.gov
|access-date=12 October 2009
|last=Herring
|first=David
|date=29 January 2001
}}
</ref>

Precision agriculture uses technology on agricultural equipment (e.g. tractors, sprayers, harvesters, etc.):
* [[positioning system]] (e.g. [[GPS]] receivers that use satellite signals to precisely determine a position on the globe);
* [[geographic information systems]] (GIS), i.e., software that makes sense of all the available data;
* variable-rate farming equipment ([[seeder]], [[Broadcast spreader|spreader]]).