Study On Optical Measurement Method Of Leaf Moisture Content Of Lettuce

Lettuce is one of the important varieties of vegetables,the moisture content of lettuce leaves is an important indicator of lettuce growth.the amount of moisture directly affects the growth of crops.The traditional methods for detecting the moisture content of crops include drying,water physiological index measurement,visual inspection of crop morphological index and crop-related water physiological index.Compared with traditional information,spectral digital image processing technology has many advantages.It changes the speed of traditional technology,the complexity of the process and the poor real-time performance,which improves the measurement efficiency of the moisture content of crop leaves.The main research contents of this paper are as follows:(1)Using 7 different spectral pre-processing data and the full-band raw data for partial least squares modeling,selecting the best pre-processing method,extracting characteristic wavelengths and modeling,and combining PCA algorithm to select the best feature bands.It found that the reflectance at the full band was inversely related to the dry basis moisture content.The spectral raw data is preprocessed by convolution smoothing,multivariate scattering correction,convolution smooth first order differential method,convolution smooth second order differential method,derivative method,mean centering and standard normalization.Then,using modeling and model evaluation criteria,the model correlation coefficient R_C is the highest and 0.8132,the standard deviation SEC is 0.0743,and the correlation coefficient R_P is 0.7936,the standard deviation SEP is 0.0578,convolution smoothing is the best preprocessed method.The correlation coefficient method was used to select 355nm,545m,680nm,709nm,844nm,935nm,and 1025nm as the characteristic bands,and the feature band modeling was performed,the characteristic band modeling correlation coefficient R_C is 0.8234,and the standard deviation SEC is 0.06732.Finally,Combining PCA algorithm to select 709nm with the highest correlation coefficient and the highest PCA scoring coefficient as the central source of experimental features.(2)Collect leaf image information under three different light source experimental environments(only the characteristic light source in the darkroom environment,the characteristic light source combined with the filter with 709nm as the center band,the bandwidth of±7nm and the experimental halogen lamp combined with the filter).Then select the best experimental environment.The same algorithm is used to extract the parameters of the image,control the different illumination conditions as the unique variable,fit the experimental data and analysis.The results show that the experimental results are best under the environment of the characteristic light source combined with the filter,and before the image parameter extraction,the target extraction,gray binarization,expansion corrosion and opening operation,image inversion and other preprocessing operations are performed.Then,the red channel values are extracted and compared using Gaussian,nonlinear fitting,and linear fitting,respectively.Finally,the results show that the highest correlation coefficients R~2 for different methods are 0.8015,0.8826 and 0.8063.(3)Each sample is predicted using four different mathematical models.It is found that the nonlinear fit of the single-channel R value and the dry-based moisture content under the characteristic light 709nm combined with the filter environment is best,and its prediction ability is superior to other models.Based on the spectral characteristics,the method of detecting the moisture content of lettuce leaves is studied.Compared with the traditional detection method,the method is fast and easy to operate,and the cost is low and easy to promote.It provides a theory for the design and development of portable crop moisture meter in the future.