High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis
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 equipm...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-05-01
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| Series: | Plant Methods |
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| Online Access: | http://link.springer.com/article/10.1186/s13007-020-00605-5 |
| _version_ | 1818214104538546176 |
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| author | Emina Mulaosmanovic Tobias U. T. Lindblom Marie Bengtsson Sofia T. Windstam Lars Mogren Salla Marttila Hartmut Stützel Beatrix W. Alsanius |
| author_facet | Emina Mulaosmanovic Tobias U. T. Lindblom Marie Bengtsson Sofia T. Windstam Lars Mogren Salla Marttila Hartmut Stützel Beatrix W. Alsanius |
| author_sort | Emina Mulaosmanovic |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T06:14:53Z |
| format | Article |
| id | doaj.art-5e54771949104495b5631b953b76ecc1 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-12-12T06:14:53Z |
| publishDate | 2020-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-5e54771949104495b5631b953b76ecc12022-12-22T00:35:04ZengBMCPlant Methods1746-48112020-05-0116112210.1186/s13007-020-00605-5High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysisEmina Mulaosmanovic0Tobias U. T. Lindblom1Marie Bengtsson2Sofia T. Windstam3Lars Mogren4Salla Marttila5Hartmut Stützel6Beatrix W. Alsanius7Department of Biosystems and Technology, Microbial Horticulture Unit, Swedish University of Agricultural SciencesDepartment of Crop Production Ecology, Plant Ecology Unit, Swedish University of Agricultural SciencesDepartment of Plant Protection Biology, Chemical Ecology Unit, Swedish University of Agricultural SciencesDepartment of Biological Sciences, State University of New York at OswegoDepartment of Biosystems and Technology, Microbial Horticulture Unit, Swedish University of Agricultural SciencesDepartment of Plant Protection Biology, Resistance Biology Unit, Swedish University of Agricultural SciencesInstitute of Horticultural Production Systems, Gottfried Wilhelm Leibniz University HannoverDepartment of Biosystems and Technology, Microbial Horticulture Unit, Swedish University of Agricultural SciencesAbstract 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.http://link.springer.com/article/10.1186/s13007-020-00605-5DamageImage analysisLeaf scaleLeafy vegetablesLesionsSpinach |
| spellingShingle | Emina Mulaosmanovic Tobias U. T. Lindblom Marie Bengtsson Sofia T. Windstam Lars Mogren Salla Marttila Hartmut Stützel Beatrix W. Alsanius High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis Plant Methods Damage Image analysis Leaf scale Leafy vegetables Lesions Spinach |
| title | High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| title_full | High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| title_fullStr | High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| title_full_unstemmed | High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| title_short | High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| title_sort | high throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis |
| topic | Damage Image analysis Leaf scale Leafy vegetables Lesions Spinach |
| url | http://link.springer.com/article/10.1186/s13007-020-00605-5 |
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