Skewed distribution of leaf color RGB model and application of skewed parameters in leaf color description model

Abstract

Background: Image processing techniques have been widely used in the analysis of leaf characteristics. Earlier techniques for processing digital RGB color images of plant leaves had several drawbacks, such as inadequate de-noising, and adopting normal-probability statistical estimation models which have few parameters and limited applicability. Results: We confirmed the skewness distribution characteristics of the red, green, blue and grayscale channels of the images of tobacco leaves. Twenty skewed-distribution parameters were computed including the mean, median, mode, skewness, and kurtosis. We used the mean parameter to establish a stepwise regression model that is similar to earlier models. Other models based on the median and the skewness parameters led to accurate RGB-based description and prediction, as well as better fitting of the SPAD value. More parameters improved the accuracy of RGB model description and prediction, and extended its application range. Indeed, the skewed-distribution parameters can describe changes of the leaf color depth and homogeneity. Conclusions: The color histogram of the blade images follows a skewed distribution, whose parameters greatly enrich the RGB model and can describe changes in leaf color depth and homogeneity.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB model to Micrometeorology Environment in Begonia Fimbristipula Hance

“…The changing physiological parameters are result from variation of temperature, radiation, humidity rapidly and instantaneously (Hand, 1988;Shamshiri et al, 2018Li at al., 2018Chand Singh et al, 2018. Non-effectively environmental factors controlling restrict the improvement of glass greenhouse yield and crop quality (Zhang et al, 2020). Therefore, Coupling the relationship between plant growth state indicated by color parameters and meteorological factors controlling help promoting the high quality and e ciency production in facility plants, realizing the precise controlling of facility environment and decreasing the cost.…”

“…Establishment of canopy color gradation skewness-distribution (CGSD) parameters table. The mean, median, mode, std, skewness and kurtosis functions were used to acquire the mean median mode standard Deviation skewness and kurtosis of the double precision arrays of red, green and blue channels, as well as gray-level images (Chen et al, 2020). The CGSD parameters were obtained, including R Mean , R Median , R Mode , R Skewness , R Kurtosis , G Mean , G Median , G Mode , Linear prediction models establishment.…”

“…Although the skewness of the red, green channels, and the gray-level image perform well, the between-group and outside-group prediction accuracy were generally lower than other CGSD parameters. The skewness is non-linear to microclimate factors (Chen et al, 2020). Therefore, we t the polynomial Z1-Z4 that incorporates spatial bidirectional patterns for the RGB channels, and the gray-level image, by taking 2 accumulation factors (the accumulate temperature (x 1 ), the accumulate photosynthetically active radiation (x 5 ) as the independent variables, which had the highest absolute value of the correlation coe cient.…”

“…Therefore, the image color information parameters are used as representative references to re ect meteorological environment of the growing plant. Skewed analysis expanded the RGB model parameters from the mean to the mean, median, mode, skewness and kurtosis, with the number of parameters from 4 to 20 (Chen et al, 2020). The color gradation skewness-distribution (CGSD) parameters describe leaf color information more accurately and comprehensively than the traditional mean parameters (Chen et al, 2020).…”

“…Skewed analysis expanded the RGB model parameters from the mean to the mean, median, mode, skewness and kurtosis, with the number of parameters from 4 to 20 (Chen et al, 2020). The color gradation skewness-distribution (CGSD) parameters describe leaf color information more accurately and comprehensively than the traditional mean parameters (Chen et al, 2020). Whether the color gradation distribution of single plant varies the same as the leaf, and the eld scale determines its sensitivity on the response of environmental meteorological factors.…”