豌豆苗期田间杂草识别与变量喷洒控制系统

Weed Recognition from Pea Seedling Images and Variable Spraying Control System

以图像实时控制器CVS-1456为核心设计了图像实时识别与变量喷洒系统。在普通光照下分别采集包含豌豆苗、土壤背景、杂草(刺儿菜)等的原始图像,分析其颜色模型,根据色差分量R-B颜色特征采用LabVIEW和IMAQ Vision编程实现杂草实时识别。基于Canny算子对识别的杂草进行边缘检测,并提取目标杂草的面积、密度和形心位置3个特征参数为变量喷洒定位提供依据。随机试验表明:基于R-B色差分量对豌豆苗期复杂背景下刺儿菜杂草平均正确识别率达到83.5%,均方差0.066,该方法准确可靠。

The application system of real-tlme image recognition and variable spraying was designed based on the virtual image real-time controller CVS - 1456. The original images, which contained pea seedlings, soil background, weed of cephalanoplos segetum, etc, were collected in normal sunlight. The color models of original images were analyzed and real-time weed recognition was realized based on R -B color features by using LabVIEW software and IMAQ Vision toolbox. The Canny algorithm was employed to detect weed edges, and three characteristic parameters of target weed, namely area, density, centroidal position, were extracted to provide positioning evidences for variable spraying. The random tests verified the accuracy and reliability of the purposed cephalanoplos segetum recognition method from complex background images based on R - B color features, in which the average right recognition rate was 85.5 % , mean square deviation 0. 066.