The comparison of pixel-based image analysis for detection of weeds in winter wheat from UAV imagery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43210%2F24%3A43926038" target="_blank" >RIV/62156489:43210/24:43926038 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.15439/2024F4147" target="_blank" >http://dx.doi.org/10.15439/2024F4147</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15439/2024F4147" target="_blank" >10.15439/2024F4147</a>
Alternative languages
Result language
angličtina
Original language name
The comparison of pixel-based image analysis for detection of weeds in winter wheat from UAV imagery
Original language description
Creating weed maps directly by growers is becoming increasingly common. In this study, an unmanned aerial vehicle (UAV) imaged a field infested by field thistle (Cirsium arvense). This paper compares four detection methods that can be used concerning agricultural practice. Two algorithms are supervised classification methods - Maximum Likelihood (ML) and Supported Vector Machine (SVM). The Pix4Dfields (Magic Tool) classification algorithm and the thresholding method are other methods used. The Kappa coefficient and the overall accuracy determined the accuracy of the individual algorithms. The highest accuracy was achieved by the thresholding method, and the lowest by the Pix4Dfields algorithm. Among the supervised classification methods, SVM achieved higher accuracy than the ML algorithm. In terms of using the methods in practice, the thresholding method proved more effective than supervised classification methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
40101 - Agriculture
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)
ISBN
978-83-969601-8-4
ISSN
2300-5963
e-ISSN
—
Number of pages
5
Pages from-to
683-687
Publisher name
Polskie Towarzystwo Informatyczne
Place of publication
Varšava
Event location
Bělehrad
Event date
Sep 8, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001413201600083