基于图像处理的青冈栎叶绿素含量检测系统研究

为了实时、便捷、经济地获取植物叶绿素含量,研究了基于OPENCV机器视觉库的青冈栎叶绿素含量实时检测系统。首先通过数码照相机获得叶片图像,对图像进行阈值分割,图像噪声处理和图像遍历,获得图像R、G、B值。然后对图像R、G、B进行各种组合变化获到不同的图像颜色特征参数,分析各图像颜色特征参数与青冈栎叶片叶绿素含量的相关性,并对相关系数较高的叶片图像颜色特征参数与叶绿素含量进行拟合分析,结果显示图像特征参数R、R-B、(R-B)/(R+B)均达到非常显著相关。在此基础上建立叶绿素含量检测模型,基于C++程序语言,OPENCV视觉库以及QT4界面程序,编写青冈栎叶绿素含量检测系统。最后将系统检测结果与其他方法进行了比较,系统检测结果平均误差为7.19%,最大误差为12.65%,验证了该系统的有效性和准确性。

Detection System of Chlorophyll Content of Cyclobalanopsis glauca Using Image Processing Technology

In order to obtain plant chlorophyll content in real time, by convenient and economic way, this paper studied the real-time detection system for cyclobalanopsis glauca chlorophyll content based on machine vision library OPENCV. Firstly, blade image was acquired through a digital camera, image R, G, B value was obtained after dealing with threshold segmentation, noise processing and image traversal. Then, different image color characteristic parameters were got from a variety changes combination of image R, G, B. The correlation of image color feature parameters and chlorophyll content of cyclobalanopsis glauca were analyzed, and high correlation coefficient of leaf image color characteristic parameters and chlorophyll content were analyzed through fitting analysis. The results showed that the image feature parameters R, R-B, (R-B)/(R+B) were very significantly correlated. Based on that, the chlorophyll content detection model was established. In addition, the detecting system was written by C++, OPENCV and QT4. Finally, the system detecting results were compared with that by the other methods; average error of system detecting result was found out to be 7.19%; and maximum error was 12.65%, proving the validity and accuracy of this system.