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.

中文翻译：

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叶色RGB模型偏态分布及偏态参数在叶色描述模型中的应用

背景图像处理技术已广泛应用于叶片特征分析。早期处理植物叶片数字 RGB 彩色图像的技术存在一些缺点，例如去噪不充分，采用的正态概率统计估计模型参数少，适用性有限。结果我们证实了烟叶图像的红、绿、蓝和灰度通道的偏度分布特征。计算了 20 个偏态分布参数，包括平均值、中位数、众数、偏度和峰度。我们使用均值参数建立了一个类似于早期模型的逐步回归模型。基于中值和偏度参数的其他模型导致基于 RGB 的准确描述和预测，以及更好地拟合SPAD值。更多的参数提高了 RGB 模型描述和预测的准确性，扩展了其应用范围。事实上，偏态分布参数可以描述叶色深度和均匀性的变化。结论叶片图像的颜色直方图呈偏态分布，其参数极大地丰富了RGB模型，可以描述叶片颜色深度和同质性的变化。