The comparison of pixel-based image analysis for detection of weeds in winter wheat from UAV imagery

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

The comparison of pixel-based image analysis for detection of weeds in winter wheat from UAV imagery

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

The comparison of pixel-based image analysis for detection of weeds in winter wheat from UAV imagery

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

ISBN

978-83-969601-8-4

ISSN

2300-5963

e-ISSN

—

Počet stran výsledku

5

Strana od-do

683-687

Název nakladatele

Polskie Towarzystwo Informatyczne

Místo vydání

Varšava

Místo konání akce

Bělehrad

Datum konání akce

8. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001413201600083