PART 1
Original video: https://www.youtube.com/watch?v=i7fZhor-P-o
PART 2
Original video: https://www.youtube.com/watch?v=lStkk-8XzK8&t=9s
Tobacco is one of the important economic crops all over the world. Tobacco mosaic virus (TMV) seriously affects the yield and quality of tobacco leaves. The expression of TMV in tobacco leaves can be analyzed by detecting green fluorescence-related traits after inoculation with the infectious clone of TMV-GFP (Tobacco mosaic virus - green fluorescent protein).
However, traditional methods for detecting TMV-GFP are time-consuming and laborious, and mostly require a lot of manual procedures. In this study, we develop a low-cost machine-vision-based phenotyping platform for the automatic evaluation of fluorescence-related traits in tobacco leaf based on digital camera and image processing.
A dynamic monitoring experiment lasting 7 days was conducted to evaluate the efficiency of this platform using Nicotiana tabacum L. with a total of 14 samples, including the wild-type strain SR1 and 4 mutant lines generated by RNA interference technology.
As a result, we found that green fluorescence area and brightness generally showed an increasing trend over time, and the trends were different among these SR1 and 4 mutant lines samples, where the maximum and minimum of green fluorescence area and brightness were mutant-4 and mutant-1 respectively.
In conclusion, the platform can full-automatically extract fluorescence-related traits with the advantage of low-cost and high accuracy, which could be used in detecting dynamic changes of TMV-GFP in tobacco leaves.
This video is about the acquisition process of software interface, the comparison of fluorescent images and RGB images of the same tobacco leaf and the change of green fluorescence of SR1 and 4 mutant lines. The comparison results of the same leaf, including the obtained fluorescent and RGB images at the first day, the third day, and the fifth day, respectively. It could be seen intuitively that fluorescent images taken by this platform showed great differences, while there was no difference between the RGB images. This result indicated that the platform could detect the changes of green fluorescence in tobacco leaves through fluorescent images. Therefore, through this system, we can use cheap and simple equipment to observe the change of green fluorescence in tobacco leaves which was invisible by human eyes.
Keywords: green fluorescence, digital camera, image processing, tobacco, phenotyping platform
Citation: Ye J, Song J, Gao Y, Lu X, Pei W, Li F, Feng H and Yang W (2022) An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves. Front. Plant Sci. 13:968855. doi: 10.3389/fpls.2022.968855
Received: 14 June 2022; Accepted: 15 August 2022;
Published: 02 September 2022.
Attribution 4.0 International — CC BY 4.0 - Creative Commons
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