Abstract
Glyphosate is a widely used nonselective herbicide. Probing the glyphosate tolerance mechanism is necessary for the screening and development of resistant cultivars. In this study, a hyperspectral image was used to develop a 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. Six days after glyphosate treatment, glyphosate-sensitive seedlings exhibited significant changes in leaf reflection and photosynthetic activity. By updating source domain and transfer component analysis, LCC prediction model performance was improved effectively (the coefficient of determination value increased from 0.65 to 0.84). Based on the predicted LCC and ChlF data, glyphosate-sensitive plants are too fragile to protect themselves from glyphosate stress, while glyphosate-resistant plants were able to maintain normal photosynthetic physiological activity. JIP-test parameters, φE0, VJ, ψE0, and M0, were used to indicate the degree of plant damage caused by glyphosate. This study constructed a transferable model for LCC monitoring to finally evaluate glyphosate tolerance in a time-series manner and verified the feasibility of ChlF in screening glyphosate-resistant cultivars.