Analysis of drought stress in strawberry based on dynamic fluorescence index
JING Min1,2*, MA Zhenyuan1,2, YANG Fan1,2, ZHANG Qi1,2, DING Min1,2, CHEN Manlong1,2
1. School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, Shaanxi 723000, China;2. Key Laboratory of Industrial Automation in Shaanxi Province, Hanzhong, Shaanxi 723000, China
Abstract：A chlorophyll fluorescence image acquisition system of strawberry seedlings was designed for investigating the relationship between drought stress and fluorescence image parameters of strawberry seedlings. The strawberry seedlings under different stress components were actively excited by using a 460 nm LED light source, and the fluorescence images from excitation to the steady-state stage were collected. Based on maximum interclass variance method, a threshold segmentation method and image de-background method were proposed for image preprocessing. The fluorescence quenching curve was plotted according to the mean pixel value of each image. The correlation between fluorescence decay ratio, dynamic fluorescence index and stress days was analyzed. The results show that the fluorescence quenching curve of isolated leaves has a large variation in decay, and the fluorescence decay ratio was highly correlated with the time of drought stress with a coefficient of determination R2=0.98. Comparing with the control group, the fluorescence decay ratio of living strawberry seedlings changes significantly within 7 days of drought stress. The dynamic fluorescence index at 550 s of the fluorescence quenching curve has a high correlation with the number of days of drought stress(coefficient of determination R2=0.84)that, which can be used as a model analyzing the drought condition of strawberry seedlings. The digitally processed fluorescence images can directly analyze the two-dimensional fluorescence distribution of leaf fluorescence and compare fluorescence information of leaves between different stress days. The chlorophyll fluorescence analysis combined with the digital images is more economical and intuitive than the traditional fluorescence detection method. It can be used as an agricultural production monitoring method to realize drought stress early warning.
