Open access publications in the SLU publication database

Abstract

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 diferentiation 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 afected. To accurately detect and quantify
damage on leaf scale, including microlesions, reliable diferentiation 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 quantifcation 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 identifes leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantifcation on two feld-grown leafy vegetable species, spinach and Swiss
chard.
Conclusions: Our novel lesion quantifcation method can be used for detection of large (macro) or single-cell
(micro) lesions on leaf scale, enabling quantifcation 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
sevn.erity of leaf damage at fne resolutio