ArticleThe plant pathology journal2016
Visual Analysis for Detection and Quantification of Pseudomonas cichorii Disease Severity in Tomato Plants.
Article in The plant pathology journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 30 citations in OpenAlex.
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Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (≥ 10(6) cfu/ml) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (ΦPSII) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII (Fv/Fm) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.
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