| Mungbean is one of the main edible legumes in China,and it is a medicinal and edible food that is favored by consumers.Salt stress seriously affects the yield and quality of mungbeans.The screening of salt-tolerant germplasm is of great significance for cultivating salt-tolerant varieties,increasing yield and promoting production development of mungbean.While it is necessary to obtain a large amount of phenotypic information like plant growth and physiological status quickly and accurately in modern breeding,there are some shortcomings of long.screening cycle,large labor consuming and strong subjectivity in traditional breeding by selection Chlorophyll fluorescence and multicolor fluorescence imaging techniques,which have the characteristics of rapid,non-destructive high-efficiency,and accelerate the screening of crop phenotypes,are one of the powerful tools for studying plant phenotypes.And these technologies have been utilized widely in the plant phenotypic studies of photosynthetic physiology,abiotic stress and biotic stress.In this study,105 varieties of mungbean collected in China and abroad were used as materials to screen and identify salt tolerance.The fluorescence parameters of the 12 selected mungbean varieties were obtained using chlorophyll fluorescence and multicolor fluorescence technologies.Feasibility of using fluorescence parameters to study the response to salt stress was examined through the correlation analysis with fluorescence parameters and seedling physiological indexes.Mathematical models for salt damage monitoring and salt tolerance identification of mungbean seedlings were established using machine learning.This study provided a certain theoretical basis for the usage of chlorophyll fluorescence and multicolor fluorescence technology in plant salt stress.The main results are as follows:(1)Screening and grading of salt tolerance of 105 mungbean varieties were conducted and the best salt tolerance line(M85)was obtained.In comparison with M38,a salt-sensitive mungbean variety(M38),it was found that mung bean seedlings were under salt stress.The minimum fluorescence(Fo),the maximum fluorescence(Fm)and maximum quantum efficiency of PSII quantum yield(Fv/Fm)show changes in the canopy space.The results showed that the effect of photosynthesis of mungbean seedlings under salt stress was initially detected in the mature leaves near the leaf center.With the increase of salt concentration and treatment time,salt stress gradually affected the entire leaves of mungbean seedlings and eventually leading to plant death.(2)Through the analysis of morphological indexes,physiological indexes and fluorescence parameters of seedlings of a series of mungbean varieties under two levels of salt stress,it was found that multiple parameters from chlorophyll fluorescence and multicolor fluorescence could be used as direct indicators of salt tolerance of mungbean seedlings.Under salt stress,Fo,F440 and F520 of mungbean seedlings increased significantly,while Fm,Fv/Fm and photochemical fluorescence quenching(qL)decreased significantly.Compared with the control group,the plant height,root length,stomatal conductance and chlorophyll content of mungbean seedlings under salt stress were significantly decreased,however,SOD activity and MDA content were significantly increased.Correlation analysis found that there were correlations between some fluorescence parameters and physiological indicators.The correlation between MDA content and Fo was 0.805,the correlation between stomatal conductance and Fm and variable fluorescence(Fv)was 0.85,The correlation between SPAD value and F440 value was-0.781,and the results showed that a large number of fluorescence parameters could be used as indicators of mungbean seedlings responsing to salt stress.(3)Through integration of chlorophyll fluorescence and multicolor fluorescence data,using principal component analysis(PCA)for data dimensionality reduction processing.Using machine learning to build a classification models for conducting early monitoring of salt stress and identification of salt tolerance of mungbean seedlings.The models were evaluated by cross-validation and chaotic matrix,and it was found that models of classification under the support vector machine(SVM)and k-nearest neighbor classification(kNN)were the best.All the six classification models with different salinity and treatment time all showed good classification capabilities.The accuracy of the five classification models were above 0.918.The model with high salinity and long-term salt stress(T2D2)had the worst classification accuracy,and the rate is 0.856.All models had the best effect of judging whether mungbean seedlings were exposed to salt stress,and the true positive rate was close to 100%.The model judges the effect of salt tolerance of different mungbean varieties was decreasing with the increase of salt concentration and treatment time.The model(T1D1)had the best comprehensive performance after 100 mmol/L salt stress for 3 days. |
