Pavicic Mirko, Overmyer Kirk, Rehman Attiq Ur, Jones Piet, Jacobson Daniel, Himanen Kristiina
Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, USA.
Department of Agricultural Sciences, Viikki Plant Science Centre, Faculty of Agriculture and Forestry, University of Helsinki, Latokartanonkaari 7, 00790 Helsinki, Finland.
Plants (Basel). 2021 Jan 15;10(1):158. doi: 10.3390/plants10010158.
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant (Arabidopsis). Arabidopsis and the model fungus (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F/F) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
基于图像的植物病害症状评分是将抗病性与植物基因型相关联的有力工具。技术进步催生了用于时程实验统计分析的新成像和图像处理策略。有多种工具可用于分析作物叶片和果实上的症状，但适用于模式植物（拟南芥）的却很少。拟南芥和模式真菌（灰霉病菌）构成了一个强大的模式病理系统，用于鉴定赋予对这种广寄主范围坏死营养型真菌免疫性的信号通路。在此，我们提出两种策略来评估灰霉病菌在拟南芥叶片中随时间的感染严重程度和症状进展。因此，使用了一种基于红-绿-蓝（RGB）图像颜色色调值和随机森林算法的像素分类策略来确定坏死、褪绿和健康叶片区域。其次，利用叶绿素荧光（ChlFl）成像，测定了光系统II的最大量子产率（F/F），以定义病害区域及其占总叶面积的比例。RGB成像和ChlFl成像策略均用于跟踪病害随时间的进展。这为检测敏感或抗性遗传背景提供了一种强大且灵敏的方法。本文描述了从植物培养到数据分析的完整方法流程。
