Pathogenicity assays on Arabidopsis thaliana infected with Xanthomonas campestris pv. campestris seize aggressiveness variation at the race and isolate levels

Távora, Fabiano Touzdjian Pinheiro Kohlrausch; Moura, Dayane R.; Cury, Natália F.; Mendes, Pollyana N.; Quezado-Duval, A. M.; Mehta, Ângela

doi:10.1007/s10658-022-02544-4

Cited by 1 publication

(1 citation statement)

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“…Interestingly, visual symptoms caused by biological treatments in oilseed rape plants did not differ from one Xcc race to another under any climatic condition, unlike broccoli [ 48 ] or Arabidopsis [ 89 ] plants, where it was possible to see differences in symptoms caused by races 1 and 4. This could compromise the ability of the classifying algorithms to distinguish plants infected with both races, as will be discussed later.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision

Pineda

Barón

2023

Plants

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Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.

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Section: Discussionmentioning

confidence: 99%