Abstract

Tobacco is one of the important economic crops all over the world. (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 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.