基于机器视觉的棉花群体叶绿素监测

研究了利用机器视觉技术快速获取棉花群体叶绿素信息的方法，以期获得预测性高的颜色特征参数。结果表明，RGB颜色系统的G－R、(G－R)/(G+R)、r与g的组合值和棉花功能叶叶绿素含量、群体绿色指数呈极显著相关，而且拟合度较高；HIS颜色系统的Hue值和棉花功能叶叶绿素含量、群体绿色指数之间也极显著相关。对筛选出的两组模型进行检验，预测精度在84.07%~93.04%之间，推荐预测精度最高的G－R参数作为获取棉花群体叶绿素信息的最佳颜色指标。G－R预测叶绿素含量和群体绿色指数的模型分别为y=－1.3008+0.2125(G－R)－0.0038(G－R)2（R²=0.8669**）和y=－0.9726+0.1227 (G－R)－0.0016(G－R)²（R²=0.7487**）。

Obtaining Information of Cotton Population Chlorophyll by Using Machine Vision Technology

Machine vision have been successful applied to monitor crop morphological and physiological status,such as leaf area index,nitrogen content,chlorophyll content and so on.The trials was conducted from 2004 to 2006 at the experiment station of Shehezi University located in Shihezi,Xingjiang Province to monitor cotton canopy chlorophyll information under field conditions by processing cotton population digital image,which is one measurement method with advantages of convenient,real time,quick and nondestructive.In order to obtain uniform cotton canopy digital images,the assistant device was used.In the field,the cotton canopy images were taken by a digital photo camera(OLYMPUS) at the squaring stage,early flowering stage,full flowering stage,peak boll stage and opening boll stage,respectively.The color characteristics of cotton population images were extracted with the image processing software developed by our lab.The correlation between color parameters of cotton canopy digital image and chlorophyll content of cotton functional leaf was analyzed.The results showed that the correlation of the color characteristics such as G-R,(G-R)/(G R), r/g,g/r,and g-r in the RGB color system,and Hue in the HIS color system with chlorophyll content of functional leaf was significant at P<0.01.There was also a significant correlation between the population greenness index(PGI) and color parameter.The chlorophyll predicted models were established.The tested results about the regression models suggested that G-R was the best parameter to monitor cotton population chlorophyll information.The relative error of chlorophyll content and PGI estimations was about 6.96% and 11.60%,and RMSE was 0.1138 and 0.1643.The chlorophyll content predicted model was y=-1.3008 0.2125(G-R)-0.0038(G-R)2(R2=0.8669**),and PGI predict model was y=-0.9726 0.1227(G-R)-0.0016(G-R)2(R2=0.7487**).