Color vegetation indices enable various precision agriculture applications by transforming a 3D-color image into its 1D-grayscale counterpart, such that the color of vegetation pixels can be accentuated, while those of nonvegetation pixels are attenuated. The quality of the transformation is essential to the outcomes of computational analyses to follow. The objective of this article is to propose a new vegetation index, the Elliptical Color Index (ECI), which leverages the quadratic discriminant analysis of 3D-color images along a normalized red ( r )—green ( g ) plane. The proposed index is defined as an ellipse function of r and g variables with a shape parameter. For comparison, the ECI’s performance was evaluated along with six other indices, by using 240 color images as a test sample captured from four vegetation species under different illumination and background conditions, together with the corresponding ground-truth patterns. For comparative analysis, the receiver operating characteristic (ROC) and the precision–recall (PR) curves helped quantify the overall performance of vegetation segmentation across all of the vegetation indices evaluated. For a practical appraisal of vegetation segmentation outcomes, this paper applied Gaussian filtering, and then the thresholding method of Otsu, to the grayscale images transformed by each of the indices. Overall, the test results confirmed that ECI outperforms the other indices, in terms of the area under the curves of ROC and PR, as well as other performance metrics, including total error, precision, and F-score.

中文翻译：

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一种新的植被分割和分类颜色索引

彩色植被指数通过将 3D 彩色图像转换为其 1D 灰度图像来实现各种精准农业应用，这样可以强调植被像素的颜色，而减弱非植被像素的颜色。转换的质量对于后续计算分析的结果至关重要。本文的目的是提出一种新的植被指数，即椭圆颜色指数 (ECI)，它利用了沿归一化红色 (r)-绿色 (g) 平面的 3D 彩色图像的二次判别分析。建议的索引定义为具有形状参数的 r 和 g 变量的椭圆函数。为了进行比较，ECI 的表现与其他六个指数一起进行了评估，通过使用 240 张彩色图像作为测试样本，在不同光照和背景条件下从四种植被物种中捕获，以及相应的地面实况模式。对于比较分析，接收器操作特征 (ROC) 和精确召回 (PR) 曲线有助于量化所有评估的植被指数中植被分割的整体性能。为了对植被分割结果进行实际评估，本文将高斯滤波，然后是 Otsu 的阈值方法，应用于每个指数转换的灰度图像。总体而言，测试结果证实 ECI 在 ROC 和 PR 曲线下面积以及其他性能指标（包括总误差、精度和 F 值）方面优于其他指标。