Time-series monitoring of transgenic maize seedlings phenotyping exhibiting glyphosate tolerance

Developing herbicide resistant cultivars is one of the effective methods to solve the safety problem caused by the use of herbicide. In this study, hyperspectral image was used to develop more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate-sensitive maize seedlings, and applying chemometrics methods to extract time-series features to screen resistant cultivars. Both the proposed two transfer strategies achieved the best prediction of LCC with a coefficient of determination value of 0.84, and relative root mean square error of 4.03 for the prediction set. Based on the predicted LCC and ChlF data, we found the antioxidant system of glyphosate-sensitive plants is too fragile to protect themselves from the damage, while glyphosate-resistant plants could overcome it by activating more powerful antioxidant system. {varphi}E0, VJ, {psi}E0, and M0 could be used to indicate damage caused by glyphosate to differentiate resistant cultivars. This study provided a new methodology to monitor LCC to finally analyze glyphosate tolerance in a time-series manner, and verified the feasibility of ChlF in crop breeding. HighlightThis study proposed a new methodology to monitor leaf chlorophyll content to finally analyze glyphosate tolerance in vivo, and verified the feasibility of chlorophyll a fluorescence in crop breeding.