Direct quantitative evaluation of disease symptoms on living plant leaves growing under natural light
Leaf color is an important indicator when evaluating plant growth and responses to biotic/abiotic stress. Acquisition of images by digital cameras allows analysis and long-term storage of the acquired images. However, under field conditions, where light intensity can fluctuate and other factors (sha...
| Veröffentlicht in: | Breeding science. - 1998. - 67(2017), 3 vom: 25. Juni, Seite 316-319 |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2017
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| Zugriff auf das übergeordnete Werk: | Breeding science |
| Schlagworte: | Journal Article disease symptom field and greenhouse image analysis plant leaf color |
| Zusammenfassung: | Leaf color is an important indicator when evaluating plant growth and responses to biotic/abiotic stress. Acquisition of images by digital cameras allows analysis and long-term storage of the acquired images. However, under field conditions, where light intensity can fluctuate and other factors (shade, reflection, and background, etc.) vary, stable and reproducible measurement and quantification of leaf color are hard to achieve. Digital scanners provide fixed conditions for obtaining image data, allowing stable and reliable comparison among samples, but require detached plant materials to capture images, and the destructive processes involved often induce deformation of plant materials (curled leaves and faded colors, etc.). In this study, by using a lightweight digital scanner connected to a mobile computer, we obtained digital image data from intact plant leaves grown in natural-light greenhouses without detaching the targets. We took images of soybean leaves infected by Xanthomonas campestris pv. glycines, and distinctively quantified two disease symptoms (brown lesions and yellow halos) using freely available image processing software. The image data were amenable to quantitative and statistical analyses, allowing precise and objective evaluation of disease resistance |
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| Beschreibung: | Date Revised 30.09.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1344-7610 |
| DOI: | 10.1270/jsbbs.16169 |