基于籽粒RGB图像独立分量的玉米胚部特征检测

Detection of embryo based on independent components for kernel RGB images in maize

玉米胚部特征是重要的农艺性状之一,目前主要通过手工方法进行测量。为实现通过机器视觉图像处理的方法进行玉米胚部特征的自动检测,提出一种基于独立分量分析ICA的玉米胚部测量方法,并建立了检测模型。首先对玉米籽粒的RGB图像进行ICA分析,发现具有最大熵的独立分量IC代表着胚部与籽粒其他部分的对比。根据此IC能够实现玉米胚部的准确分割。然后,提取了玉米胚部面积等9个特征。和手工检测结果相比,面积误差为0.7%,决定系数达0.984,其他8个特征的误差总体也都在2%以下。与前人的基于颜色模型区域生长的检测结果比较,检测准确度有明显提高。表明采用基于ICA的方法检测的结果准确可靠,能够用于玉米胚部的自动检测。

The characteristics of maize embryo are important agronomic traits of maize,which are mainly measured by hand. In order to implement automatic extraction of the features of maize embryo by computer vision and image processing method,a new method for measuring embryo based on independent component analysis（ICA） was developed,and its testing model was also established. RGB images of 40 maize kernels were scanned with 600 DPI resolutions using a flat scanner. After segmenting embryo part from other parts of maize kernels using the independent component with the maximum entropy,the embryo area and the other 8 embryo characteristics of these maize kernels were extracted. Compared with the manual measured results as ground-truth reference,the area error rate for our proposed method was 0.7%,and determination coefficient of the manual regression to the predicted reached 0.984,and error rates of other 8 characteristics were generally below 2%. When compared with citations of those based on the region growing of color models,our proposed method significantly increased detection accuracy. Obviously,the proposed method based on ICA is accurate and reliable,and can be used to automatic detection of maize embryo.