Details
| Original language | English |
|---|---|
| Article number | 62 |
| Number of pages | 22 |
| Journal | PLANT METHODS |
| Volume | 16 |
| Publication status | Published - 4 May 2020 |
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 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.
Keywords
- Damage, Image analysis, Leaf scale, Leafy vegetables, Lesions, Spinach, Wounds
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biotechnology
- Biochemistry, Genetics and Molecular Biology(all)
- Genetics
- Agricultural and Biological Sciences(all)
- Plant Science
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In: PLANT METHODS, Vol. 16, 62, 04.05.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis
AU - Mulaosmanovic, Emina
AU - Lindblom, Tobias U.T.
AU - Bengtsson, Marie
AU - Windstam, Sofia T.
AU - Mogren, Lars
AU - Marttila, Salla
AU - Stützel, Hartmut
AU - Alsanius, Beatrix W.
N1 - Funding Information: Open access funding provided by Swedish University of Agricultural Sciences. This study was conducted within the framework of the project “Safe ready to eat vegetables from farm to fork: The plant as a key for risk assessment and prevention of EHEC infections”(acronym Safe Salad; Grant Number: 2012-2107) funded by FORMAS (The Swedish Research Council for Sustainable Development), Stockholm (PI: Beatrix Alsanius).
PY - 2020/5/4
Y1 - 2020/5/4
N2 - 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.
AB - 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.
KW - Damage
KW - Image analysis
KW - Leaf scale
KW - Leafy vegetables
KW - Lesions
KW - Spinach
KW - Wounds
UR - http://www.scopus.com/inward/record.url?scp=85084370036&partnerID=8YFLogxK
U2 - 10.1186/s13007-020-00605-5
DO - 10.1186/s13007-020-00605-5
M3 - Article
C2 - 32391069
AN - SCOPUS:85084370036
VL - 16
JO - PLANT METHODS
JF - PLANT METHODS
SN - 1746-4811
M1 - 62
ER -