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

Abstract

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.

Plant phenotyping relevance

蛍光画像、植生指数、熱画像から無症状感染植物を分類する手法を構築・評価しており、植物病害状態の抽出が中心的な技術貢献である。

Code and data availability

The paper's phenotype measurements (F520, thermography, WBI, DBI3 image-derived histogram parameters) and classifier databases are not publicly deposited; the Data Availability Statement directs inquiries to the corresponding author. The supplement (Table S1) only lists best-fit algorithm parameters, not datasets, code

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