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 thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (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_v/F_m) 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.

中文翻译：

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切除拟南芥叶片中真菌病原体症状进展和严重程度的基于图像的方法

基于图像的植物疾病症状评分是将抗病性与植物基因型相关联的强大工具。技术的进步为时程实验的统计分析提供了新的成像和图像处理策略。有几种工具可用于分析农作物的叶子和果实的症状，但是只有少数几个可用于模型植物拟南芥（Arabidopsis）。拟南芥和模式真菌灰葡萄孢（Botrytis）包括有效的模型病理系统，用于鉴定赋予针对这种广泛宿主范围的坏死性真菌的免疫力的信号传导途径。在这里，我们提出了两种策略来评估拟南芥叶片中葡萄孢感染的严重程度和症状进展。因此，使用了来自红-绿-蓝（RGB）图像的色相值和随机森林算法的像素分类策略，用于建立坏死，褪绿和健康的叶子区域。其次，使用叶绿素荧光（ChlFl）成像，光系统II的最大量子产率（F _v / F _m确定）以定义患病区域及其在总叶面积中所占的比例。RGB和Chf1I成像策略均被用来跟踪疾病随时间的进展。这提供了用于检测敏感或抗性遗传背景的鲁棒且敏感的方法。描述了从植物培养到数据分析的完整方法工作流程。