Identification of High-Variation Fields based on Open Satellite Imagery

Publikation: Forskning - peer reviewKonferenceartikel

DOI

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.
OriginalsprogEngelsk
TidsskriftAdvances in Animal Biosciences
Vol/bind8
Tidsskriftsnummer2
Sider (fra-til)388-393
Antal sider6
ISSN2040-4700
DOI
StatusUdgivet - 1 jun. 2017
BegivenhedEuropean Conference on Precision Agriculture - Edinburgh, Storbritannien
Varighed: 16 jul. 201720 jul. 2017
Konferencens nummer: 11th
https://ecpa.delegate-everything.co.uk/

Konference

KonferenceEuropean Conference on Precision Agriculture
Nummer11th
LandStorbritannien
ByEdinburgh
Periode16/07/201720/07/2017
Internetadresse

Bibliografisk note

Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017)

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