Editing Precision agriculture (section)

=== 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>