Mulaosmanovic, E.; Lindblom, T.U.T.; Bengtsson, M.; Windstam, S.T.; Mogren, L. et al.: High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis. In: Plant Methods 16 (2020), Nr. 1, 62. DOI: https://doi.org/10.1186/s13007-020-00605-5
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. © 2020 The Author(s).
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