Editing Precision agriculture (section)

==Emerging technologies==
Precision agriculture is an application of breakthrough digital farming technologies. Over $4.6 billion has been invested in agriculture tech companies—sometimes called agtech.<ref name="ey.com"/>

===Robots===
[[Self-driving tractor |Self-steering tractors]] have existed for some time now, as [[John Deere]] equipment works like a plane on [[autopilot]]. The tractor does most of the work, with the farmer stepping in for emergencies.<ref name="economist.com"/>  Technology is advancing towards driverless machinery programmed by GPS to spread fertilizer or plow land. Autonomy of technology is driven by the demanding need for diagnoses, often difficult to accomplish solely by hands-on farmer-operated machinery. In many instances of high rates of production, manual adjustments cannot be sustained.<ref>{{cite book |last1=Zhang |first1=Qin |title=Precision Agriculture Technology for Crop Farming |date=2016 |publisher=CRC Press |location=Boca Raton, FL |isbn=9781482251074 |page=134 |url= }}</ref> Other innovations include, partly solar powered, machines/robots that identify weeds and precisely kill them with a dose of a herbicide or [[Weed control #Lasers|lasers]].<ref name="economist.com"/><ref>{{cite news |last1=Papadopoulos |first1=Loukia |title=This new farming robot uses lasers to kill 200,000 weeds per hour |url=https://interestingengineering.com/innovation/farming-robot-lasers-200000-weeds-per-hour |access-date=17 November 2022 |work=interestingengineering.com |date=21 October 2022}}</ref><ref>{{cite web |title=Verdant Robotics launches multi-action agricultural robot for 'superhuman farming' |url=https://roboticsandautomationnews.com/2022/02/23/verdant-robotics-launches-multi-action-agricultural-robot-for-superhuman-farming/49471/ |website=Robotics & Automation News |access-date=17 November 2022 |date=23 February 2022}}</ref>

[[Agricultural robot]]s, also known as AgBots, already exist, but advanced harvesting robots are being developed to identify ripe fruits, adjust to their shape and size, and carefully pluck them from branches.<ref name="idealog.co.nz">{{cite web|url=http://idealog.co.nz/tech/2016/10/five-technologies-changing-agriculture|title=Five technologies changing agriculture|date=7 October 2016}}</ref>

===Drones and satellite imagery===
[[Unmanned aerial vehicle|Drone]] and [[satellite]] technology are used in precision farming. This often occurs when drones take high-quality images while satellites capture the bigger picture. Aerial photography from light aircraft can be combined with data from satellite records to predict future yields based on the current level of field [[Biomass (ecology)|biomass]]. Aggregated images can create contour maps to track where water flows, determine variable-rate seeding, and create yield maps of areas that were more or less productive.<ref name="economist.com"/>

===The Internet of things===
The [[Internet of things]] is the network of physical objects outfitted with electronics that enable data collection and aggregation. IoT comes into play with the development of sensors<ref>M. Sophocleous, Thick-Film Underground Sensors. LAP LAMPERT Academic Publishing, 2016. {{ISBN|978-3-659-95270-8}} https://www.morebooks.de/store/us/book/thick-film-underground-sensors/isbn/978-3-659-95270-8</ref> and farm-management software. For example, farmers can spectroscopically measure nitrogen, phosphorus, and potassium in [[liquid manure]], which is notoriously inconsistent.<ref name="economist.com"/> They can then scan the ground to see where cows have already urinated and apply fertilizer to only the spots that need it. This cuts fertilizer use by up to 30%.<ref name="idealog.co.nz"/> Moisture sensors<ref>M. Sophocleous and J. K. Atkinson, “A novel thick-film electrical conductivity sensor suitable for liquid and soil conductivity measurements,” Sensors Actuators, B Chem., vol. 213, pp. 417–422, 2015. https://doi.org/10.1016/j.snb.2015.02.110</ref> in the soil determine the best times to remotely water plants. The [[irrigation]] systems can be programmed to switch which side of the tree trunk they water based on the plant's need and rainfall.<ref name="economist.com"/>

Innovations are not just limited to plants—they can be used for the welfare of animals. [[Cattle]] can be outfitted with internal sensors to keep track of stomach acidity and digestive problems. External sensors track movement patterns to determine the cow's health and fitness, sense physical injuries, and identify the optimal times for breeding.<ref name="economist.com"/> All this data from sensors can be aggregated and analyzed to detect trends and patterns.

As another example, monitoring technology can be used to make beekeeping more efficient. Honeybees are of significant economic value and provide a vital service to agriculture by pollinating a variety of crops. Monitoring of a honeybee colony's health via wireless temperature, humidity, and {{CO2}} sensors helps to improve the productivity of bees, and to read early warnings in the data that might threaten the very survival of an entire hive.<ref>{{Cite web|url=https://iotone.com/casestudy/precision-beekeeping-with-wireless-temperature-monitoring/c918|title=Precision beekeeping with wireless temperature monitoring |website=IoT ONE|access-date=27 April 2018}}</ref>

=== Smartphone applications ===
[[File: Figure 16 Components of a Precision Agriculture System (49132514563).jpg|thumb|upright=1.4|A possible configuration of a smartphone-integrated precision agriculture system]]
Smartphone and tablet applications are becoming increasingly popular in precision agriculture. Smartphones come with many useful applications already installed, including the camera, microphone, GPS, and accelerometer. There are also applications made dedicated to various agriculture applications such as field mapping, tracking animals, obtaining weather and crop information, and more. They are easily portable, affordable, and have high computing power.<ref>Suporn Pongnumkul, Pimwadee Chaovalit, and Navaporn Surasvadi, “Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research,” Journal of Sensors, vol. 2015.</ref>

=== Machine learning ===
Machine learning is commonly used in conjunction with drones, robots, and internet of things devices. It allows for the input of data from each of these sources. The computer then processes this information and sends the appropriate actions back to these devices. This allows for robots to deliver the perfect amount of fertilizer or for IoT devices to provide the perfect quantity of water directly to the soil.<ref>{{Cite journal|last1=Goap|first1=Amarendra|last2=Sharma|first2=Deepak|last3=Shukla|first3=A.K.|last4=Rama Krishna|first4=C.|date=December 2018|title=An IoT based smart irrigation management system using Machine learning and open source technologies|journal=Computers and Electronics in Agriculture|volume=155|pages=41–49|doi=10.1016/j.compag.2018.09.040|bibcode=2018CEAgr.155...41G |s2cid=53787393}}</ref> Machine learning may also provide predictions to farmers at the point of need, such as the contents of plant-available [[Nitrogen cycle|nitrogen in soil]], to guide fertilization planning.<ref>{{Cite journal|last1=Grell|first1=Max|last2=Barandun|first2=Giandrin|last3=Asfour|first3=Tarek|last4=Kasimatis|first4=Michael|last5=Collins|first5=Alex|last6=Wang|first6=Jieni|last7=Guder|first7=Firat|date=9 October 2020|title=Determining and Predicting Soil Chemistry with a Point-of-Use Sensor Toolkit and Machine Learning Model|journal=bioRxiv|doi=10.1101/2020.10.08.331371|s2cid=222348520}}</ref> As more agriculture becomes ever more digital, machine learning will underpin efficient and precise farming with less manual labour.