Editing Lidar (section)

===Agriculture===
[[File:LIDAR field yield.jpg|thumb|alt=Graphic of a lidar return, featuring different crop yield rates|Lidar is used to analyze yield rates on agricultural fields.]]

[[Agricultural robot]]s have been used for a variety of purposes ranging from seed and fertilizer dispersions, sensing techniques as well as crop scouting for the task of [[weed control]].

Lidar can help determine where to apply costly fertilizer. It can create a topographical map of the fields and reveal slopes and sun exposure of the farmland. Researchers at the [[Agricultural Research Service]] used this topographical data with the farmland yield results from previous years, to categorize land into zones of high, medium, or low yield.<ref>{{cite web |url=http://www.ars.usda.gov/is/pr/2010/100609.htm |title=ARS Study Helps Farmers Make Best Use of Fertilizers |publisher=USDA Agricultural Research Service |date=June 9, 2010}}</ref> This indicates where to apply fertilizer to maximize yield.

Lidar is now used to monitor insects in the field. The use of lidar can detect the movement and behavior of individual flying insects, with identification down to sex and species.<ref>{{Cite book|first1=Alem|last1=Gebru|first2=Samuel|last2=Jansson|first3=Rickard|last3=Ignell|first4=Carsten|last4=Kirkeby|first5=Mikkel|last5=Brydegaard|title=Conference on Lasers and Electro-Optics |chapter=Multispectral polarimetric modulation spectroscopy for species and sex determination of Malaria disease vectors |date=2017-05-14 |pages=ATh1B.2 |url=https://www.osapublishing.org/abstract.cfm?uri=CLEO_AT-2017-ATh1B.2 |publisher=Optical Society of America |doi=10.1364/CLEO_AT.2017.ATh1B.2|isbn=978-1-943580-27-9|s2cid=21537355}}</ref> In 2017 a patent application was published on this technology in the United States, Europe, and China.<ref>{{cite web |url=https://patents.google.com/patent/WO2017182440A1/en |website=Google Patents |access-date=4 June 2019|title=Improvements in or relating to optical remote sensing systems for aerial and aquatic fauna, and use thereof }}</ref>

Another application is crop mapping in orchards and vineyards, to detect foliage growth and the need for pruning or other maintenance, detect variations in fruit production, or count plants.

Lidar is useful in [[GNSS]]-denied situations, such as nut and fruit orchards, where foliage causes [[Error analysis for the Global Positioning System|interference]] for agriculture equipment that would otherwise utilize a precise GNSS fix. Lidar sensors can detect and track the relative position of rows, plants, and other markers so that farming equipment can continue operating until a GNSS fix is reestablished.

Controlling weeds requires identifying plant species. This can be done by using 3-D lidar and machine learning.<ref name=":8">{{Cite book|last1=Weiss|first1=Ulrich|last2=Biber|first2=Peter|last3=Laible|first3=Stefan|last4=Bohlmann|first4=Karsten|last5=Zell|first5=Andreas|chapter=Plant Species Classification using a 3D LIDAR Sensor and Machine Learning|title=2010 International Conference on Machine Learning and Applications |year=2010 |isbn=978-1-4244-9211-4}}</ref> Lidar produces plant contours as a "point cloud" with range and reflectance values. This data is transformed, and features are extracted from it. If the species is known, the features are added as new data. The species is labelled and its features are initially stored as an example to identify the species in the real environment. This method is efficient because it uses a low-resolution lidar and supervised learning. It includes an easy-to-compute feature set with common statistical features which are independent of the plant size.<ref name=":8" />