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.

Plant phenotyping relevance

葉画像のRGB色ヒストグラムから葉色の深さ・均一性を推定する画像解析モデルを開発しており、植物表現型の取得・抽出が研究の中心です。

Code and data availability

The paper's tobacco leaf images, SPAD measurements, and MATLAB analysis are not publicly deposited; the authors state data are available only on request from the corresponding author.

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