中文摘要：

利用计算机视觉技术快速测定叶绿素含量的方法，建立了根据番茄叶片颜色特征确定其叶绿素含量的一元二次拟合模型。在计算机视觉图像采集系统中采集番茄叶片图像，利用MATLAB图像处理工具提取图像的颜色特征参数，对颜色特征参数和番茄功能叶叶绿素含量做相关分析，建立回归模型。结果表明：RGB颜色系统的R／G、（G-R）／（G＋R）、G-R、色度坐标r、r-g及HIS颜色系统的日值均与叶绿素含量呈极显著非线性相关，可用于测定番茄叶片叶绿素含量。从建立的6组模型中筛选出拟合度较高的3组模型进行检验，预测误差在0-22.22％之间。用预测精度最高的G-R颜色特征预测叶绿素含量的模型为Ch1.a=0.0926＋0.1208（G-R）-0.0009（G-R）^2，Ch1.b=-0.0252＋0.0397（G-R）-0.0003（G-R）^2和Ch1.（a＋b）=0.1271＋0.1600（G-R）-0.0011（G-R）^2。

英文摘要：

The rapid methods detecting chlorophyll concentration by the computer vision technology, and a unary quadratic model to predict chlorophyll content based on color parameters of tomato leaf images have been established in this study. The images of tomato leaves were taken in the image acquisition system, then the color characteristics were extracted with the MATLAB image processing software. The correlation be-tween color parameters of tomato digital image and chlorophyll content of tomato functional leaf were analyzed by nonlinear regress models. The results showed that the color characteristics such as R/G,（G-R）/（G＋R）, G-R, r, r-g in the RGB color system, and H-value in the HIS color system were significantly correlation with chlorophyll content of tomato leaf at P 〈0.01. Six sets of prediction model were established and a- mong them 3 models with high fitting degree were selected to use. The prediction accuracy of the selected model were tested, and error ranged 0 to 22.22%. According to the determination coefficients and RMSE （ root mean square error）, G - R was the best color characteristic to predict chlorophyll content of tomato leaf. The corresponding models are Ch1.a=0.0926＋0.1208（G-R）-0.0009（G-R）^2, Ch1.b=-0.0252＋0.0397（G-R）-0.0003（G-R）^2 and Ch1.（a＋b） =0.1271＋0.1600（G-R）-0.0011（G-R）^2.