Analysis And Study On Chlorophyll Fluorescence Imaging Of Tomato Seedlings Under Low Temperature Stress

Tomato is one of the most important vegetable crops in Solanaceae,which has the widely growing environment and is easy to cultivate.Tomato is a kind of vegetable that prefers warm environment,and the effective control and management of temperature factors directly affect the yield and quality of tomato.Studying the growth of tomato at low temperature and overcoming the low temperature barrier in the facility,and realizing the sustainable development of the facility vegetable industry is one of the urgent problems to be solved in production.In this paper,the leaves of tomato seedlings are used as research objects,and the tomato varieties “Liao yuan duo li” and “Horticultural-L404” are widely planted in the north of China.Through three consecutive years(2017?2019)experimental research,plant physiology and engineering research on tomato seedlings are carried out by using chlorophyll fluorescence kinetic parameters value,kinetic curves and chlorophyll fluorescence image data.The effects of chilling injury stress on chlorophyll fluorescence parameters,kinetic curves and chlorophyll fluorescence image characteristics of tomato seedlings are analyzed,and the cold damage of tomato seedlings is modeled and identified from three aspects: leaf damage zone,leaf damage stress time and leaf damage stress area.In this experiment,the analysis of chlorophyll fluorescence characteristics and grading of different cold damage degrees in tomato leaves under low temperature stress provide a good theoretical foundation for the identification and recovery of tomato cold damage.The main research contents and conclusions are as follows:(1)The effect of chilling injury on tomato seedlings is studied based on chlorophyll fluorescence imaging.Chlorophyll fluorescence imaging is used to study the chilling injury stress of tomato seedlings under low temperature stress.The chlorophyll fluorescence kinetic parameters valures and images of tomato seedlings are collected under the experimental environment.After lateral heterogeneity analysis of parameter values and images,the effects of different degrees low temperature stress on tomato leaves produce significant spatial heterogeneity in fluorescence parameters F,Y(II),Y(NPQ),Y(NO),q P,and q L images.The results show that the Y(II)value is sensitive to chilling injury and is not affected by leaf veins.The histogram and the mean as well as standard deviation of energy(ASM),entropy(ENT),moment of inertia(INE)and autocorrelation(COR)are shown as good indicators for detecting chilling injury stress.(2)The leaves are classified using different cold damage zones of tomato seedling leaves.The histogram clustering algorithm is used to segment the different chilling injury zones of the chlorophyll fluorescence parameter Y(II)image,and the chlorophyll fluorescence dynamics image and curve analysis are carried out.The results show that the same leaf can be divided into healthy,transitional and chilling injury zones according to the changes of Y(II)values.In the meanwhile,according to the changes of Y(II)and Y(NO)values,the tomato seedlings can be classified into five grades: healthy leaves and chilling injury 1,2,3 and 4 classes.(3)The chilling injury of tomato seedlings based on different low temperature stress time is classified and identified.Six chlorophyll fluorescence parameters: Y(II),q P,q L,Y(NPQ),Y(NO),Fv/Fm are obtained by the maximum correlation of the Pearson coefficient,and are used as the input feature parameters for automatically identifying the cold damage class of tomato seedlings.At the same time,the color descriptor feature and the grayscale image feature of the parameter Y(II)image which has the greatest correlation with time are acquired.The BP neural network recognition model is trained using the histogram skewness,the texture entropy standard deviation,the color descriptor B,the color descriptor b,and the color descriptor L/b as input features.The experimental results suggest using the color descriptor b and the color descriptor L/b of the L*a*b* color space to determine the class of chilling injury in tomato leaves.(4)The identification method based on the chilling injury area is used to classify the tomato seedling cold damage.The improved k-means++ clustering segmentation method is used to segment the image of chlorophyll fluorescence parameter F to obtain the chilling injury area in the leaf,and then the tomato seedlings were classified according to the chilling injury area to detect the class of cold damage by chlorophyll fluorescence.The results show that when the quantitative statistical analysis model is used to identify the chilling injury class,the recognition rates of healthy leaves and severe chilling injury leaf are better,and the recognition rates of the 1 and 2 classes are poorer.Moreover,the best recognition rate of training set and test set in the four recognition models is the PSO-SVM of Spearman feature selection,and the test set recognition rate can reach 98.2%.In this study,firstly,the effects of chlorophyll fluorescence parameters,kinetic curves and chlorophyll fluorescence image characteristics of tomato seedlings under low temperature chilling stress are analyzed,and the photosynthetic physiological cold damage classification of tomato seedlings is studied from different regions of Y(II)image.Then,from 12 different fluorescence parameters of tomato seedlings,the cold damage characteristics are modeled and identified from different low temperature stress times,and the chlorophyll fluorescence analysis of chilling injury photosynthetic physiology are extended.Finally,based on the stress area of F parameters image,the chilling injury of tomato seedlings was modeled and identified,and the engineering expansion of chlorophyll fluorescence research is realized.This study sets up a good theoretical foundation for improving tomato cultivation techniques.