Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera syst...

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Veröffentlicht in:Plant disease. - 1997. - 107(2023), 1 vom: 17. Jan., Seite 188-200
1. Verfasser: Barreto, Abel (VerfasserIn)
Weitere Verfasser: Ispizua Yamati, Facundo Ramón, Varrelmann, Mark, Paulus, Stefan, Mahlein, Anne-Katrin
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Plant disease
Schlagworte:Journal Article automatic scoring digital surface model multiclass classification partial least squares discriminant analysis support vector machine radial time-series unmanned aerial vehicle Sugars
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520 |a Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license 
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650 4 |a automatic scoring 
650 4 |a digital surface model 
650 4 |a multiclass classification 
650 4 |a partial least squares discriminant analysis 
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700 1 |a Ispizua Yamati, Facundo Ramón  |e verfasserin  |4 aut 
700 1 |a Varrelmann, Mark  |e verfasserin  |4 aut 
700 1 |a Paulus, Stefan  |e verfasserin  |4 aut 
700 1 |a Mahlein, Anne-Katrin  |e verfasserin  |4 aut 
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