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. 2022 Sep 2:13:968855.
doi: 10.3389/fpls.2022.968855. eCollection 2022.

An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves

Junli Ye  1 , Jingyan Song  1 , Yuan Gao  1 , Xu Lu  2   3 , Wenyue Pei  2 , Feng Li  2 , Hui Feng  1   4 , Wanneng Yang  1   4
Affiliations

An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves

Junli Ye et al. Front Plant Sci. .

Abstract

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.

Keywords: digital camera; green fluorescence; image processing; phenotyping platform; tobacco.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The prototype of fluorescence phenotyping platform: (A) Appearance of the platform, (B) the inter structure of the platform, (C) the block diagram of the acquisition part, and (D) the acquired leaf fluorescence image.
FIGURE 2
FIGURE 2
Diagram of control and data acquisition for the fluorescence phenotyping platform.
FIGURE 3
FIGURE 3
Control diagram for the digital camera.
FIGURE 4
FIGURE 4
Flow chart of image processing and data analysis to extract fluorescence-related traits. Panel (A) was the currently processed folder path. Panel (B) was the original image. Panel (C) was the cropped image. Panel (D) was the ExG image. Panel (E) was the green channel image. Panel (F) was the binary pseudo-color image. Panel (G) was the green channel image of the binary area.
FIGURE 5
FIGURE 5
The comparison of fluorescent images and RGB images of the same tobacco leaf. Panels (A–C) were fluorescent images. Panels (D–F) were RGB images.
FIGURE 6
FIGURE 6
The change of green fluorescence of SR1 and 4 mutant lines. Numbers 1 to 5 represented mutant-1, SR1, mutant-3, mutant-2 and mutant-4, respectively, and panels (a–f) represented different periods over time.
FIGURE 7
FIGURE 7
The repeatability results of two different lines. Panels (a,b) represented different repetitions of the same line, panels (c,d) represented different repetitions of another line, number 1 to 6 represented different periods.
FIGURE 8
FIGURE 8
The fluorescent images [panels (a,c,e,g,i,k)] taken by this platform and binary pseudo-color images [panels (b,d,f,h,j,l)] of tobacco leaf in the different periods (Day one to Day six).
FIGURE 9
FIGURE 9
The green fluorescence area and brightness change over time of SR1 and 4 mutant lines plants. Panel (A) showed the change of green fluorescence area, panel (B) showed the rate of green fluorescence area change per hour, panel (C) showed the change of green fluorescence brightness, and panel (D) showed the rate of green fluorescence brightness change per hour.
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