Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.

中文翻译：

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基于台盼蓝染色和数字图像分析的叶鳞病斑检测和量化的高通量方法。

背景 田间种植的多叶蔬菜可能会受到生物和非生物因素的损害，或因耕作方式而受到机械性损害。评估叶片组织损伤的现有方法主要依赖于健康和受损组织之间的颜色区分。或者，可以使用精密设备，例如显微镜和高光谱相机。根据致病因素，受伤区域的颜色变化并不总是被诱导，当症状变得明显时，植物可能已经受到严重影响。为了准确检测和量化叶尺度上的损伤，包括微损伤，健康和受损组织之间的可靠区分是必不可少的。我们用台盼蓝染料对整片叶子进行染色，这种染料可以穿过受损的细胞膜，但不会被活细胞吸收，其次是自动量化叶尺度上的损伤。结果 我们提出了一种稳健、快速和灵敏的方法，用于叶尺度可视化、准确的自动提取和叶类蔬菜叶片受损区域的测量。我们开发的图像分析管道可自动识别叶面积和单个染色（病变）区域，直至细胞水平。作为原理证明，我们测试了两种田间种植的绿叶蔬菜——菠菜和瑞士甜菜——的损伤检测和量化方法。结论 我们的新型病变量化方法可用于检测叶片尺度上的大（宏观）或单细胞（微）病变，能够在任何阶段量化病变，并且不需要症状在可见光谱内。量化叶片尺度上的受伤区域对于生成经济损失和产品保质期的预测模型是必要的。此外，风险评估基于对机会性病原体叶片损伤和感染率之间关系的准确预测，我们的方法有助于以高分辨率确定叶片损伤的严重程度。