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利用生化方法和计算机视觉评估气候变化条件下种植的油菜黑腐病

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

作者信息

Pineda Mónica, Barón Matilde

机构信息

Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), 18008 Granada, Spain.

出版信息

Plants (Basel). 2023 Mar 14;12(6):1322. doi: 10.3390/plants12061322.

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 pv. (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.

摘要

全球变暖对植物和病原体都是一项挑战,这涉及到竞争双方生理上的深刻变化,以适应新的环境条件并在它们的相互作用中取得成功。已针对油菜植株和野油菜黄单胞菌野油菜致病变种(Xcc)的两个生理小种(1和4)的行为及其相互作用开展了研究,以预测我们在未来可能的气候条件下的应对措施。在任何检测的气候条件下,Xcc的两个生理小种所引发的症状都非常相似,不过每个生理小种感染叶片后的细菌计数有所不同。气候变化使Xcc症状的出现至少提前了3天,这与氧化应激和色素组成的变化有关。Xcc感染加剧了已经由气候变化诱导的叶片衰老。为了在任何气候条件下早期识别出被Xcc感染的植株,利用从Xcc无症状叶片上记录的绿色荧光图像、两个植被指数和热成像所获得的参数,对四种分类算法进行了训练。在所有情况下,分类准确率均高于满分1.0中的0.85,在测试的气候条件下,k近邻分析和支持向量机表现最佳。

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