Evaluation of bacterial leaf spot on sweet pepper with visual assessment and multispectral sensor device
Plant diseases represent one of the main biotic stressors that, depending on the time of occurrence and intensity, lead to a reduction or complete yield losses. Phytopathogenic bacteria Xanthomonas euvesicatoria, a major causal agent of bacterial leaf spot disease (BLS) in sweet peppers causes lesions on the leaves, stems, and fruits. The aim of this study was to select spectral reflectance indices that correlate to the highest extent with the results of the visual assessment of BLS. The appearance of disease symptoms was assessed on two tolerant and two susceptible sweet pepper lines, by visual scoring according to the Horsfall-Barratt (HB) scale and by using an active, portable multispectral optical device (Plant-O-Meter), in the field under natural infection. The results indicated that vegetation indices (VIs) showed different significance depending upon the particular stage of visual assessments (V.A.). Indices, such as EVI, WDRVI, GRDVI, PNDVI were found to be the most suitable for early disease detection since they showed significant correlation at third V.A. The largest number of vegetation indices showed the highest significant correlation at 4 V.A. and 5 V.A. which indicated that they can serve in later stages of disease detection. This study provides valuable insights into the use of an active sensor device for sweet pepper bacterial leaf spot detection, monitoring, and evaluation, potentially enabling more timely and targeted interventions. Further research could explore the practical implementation of these findings in field settings and evaluate their effectiveness in large-scale disease management strategies.
Authors
- Sladjana Medic-Pap (ORCID: https://orcid.org/0000-0002-1426-1143)
- Svetlana Glogovac (ORCID: https://orcid.org/0000-0001-9902-099X)
- Bojana Ivošević (ORCID: https://orcid.org/0000-0002-6832-1893)
- Nataša Ljubičić (ORCID: https://orcid.org/0000-0001-5982-9401)
- Dario Danojević (ORCID: https://orcid.org/0000-0002-4048-8413)
Publication Details
- Journal
- Ratarstvo i povrtarstvo
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5937/ratpov62-56187
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja