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Disease incidence and severity of Cercospora leaf spot in sugar beet assessed by multispectral unmanned aerial images and machine learning

  • Autor/in: Barreto, A., F. Ispizua, M. Varrelmann, S. Paulus, A.-K. Mahlein
  • Jahr: 2022
  • Zeitschrift: Plant Disease 107
  • Seite/n: 188-200, doi.org/10.1094/PDIS-12-21-2734-RE


Disease incidence 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 (DSM), vegetation indices, shadow condition and image resolution improved classification performance in comparison to using single multispectral channels in 12% and 6% of diseased and soil regions, respectively. With a post-processing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of disease incidence (DI) and disease severity (DS) from UAV data. The calculated area under disease progress curve (AUDPC) of DS was 2810.4 to 7058.8 %.days for human visual scoring and 1400.5 to 4343.2 %.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared to visual scoring was observed in area-related parameters, like area of complete foliage (AF), area of healthy foliage (AH) and mean area of CLS lesion by unit of foliage (Ac/F). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise non-destructive assessment via multispectral data acquired by UAV flights.
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