基于计算机视觉的三七主根质量的分级方法  

Quality classification method of Panax notoginseng taproot based on computer vision

作　　者：于佳杨 王凤花[1] 张兆国[1] 杨薇[1] 朱海龙[2] 

机构地区：[1]昆明理工大学现代农业工程学院,云南昆明650500 [2]昆明理工大学工程训练中心,云南昆明650500

出　　处：《湖南农业大学学报（自然科学版）》2016年第6期682-685,共4页Journal of Hunan Agricultural University(Natural Sciences)

基　　金：国家自然科学基金项目(11226220);云南省科学技术厅项目(2010ZC028)

摘　　要：选取干燥后的三七主根样本110个,运用计算机视觉技术获取三七主根样本图像,对图像进行灰度化、二值化以及形态学运算,提取长、宽、投影面积等特征值。结果表明,三七主根的形状可分为锥形和瘤形,分别对2种主根建立投影面积和质量的关系预测模型,三七主根的质量和投影面积呈线性相关,锥形三七主根与瘤形三七主根投影面积和质量预测模型的决定系数R2分别为0.984 9和0.986 6。采用十折交叉验证法对质量预测模型进行验证,锥形三七主根质量误差均值0.334 8 g;瘤形三七主根质量误差均值0.494 9 g。In this study, 110 dried Panax notoginseng were selected as the test samples. Computer vision technology was used to obtain the images of Panax notoginseng taproot, which were deal with gray, binary and morphological to extract the length, width and projection area was ed after preprocess.The prediction models were built to calculate the projection area and the weight for cone Panax notoginseng and nodule Panax notoginseng, respectively. The results showed that the weight of mainroot was linely correlated with the projection area. The determination coefficients of cone Panax notoginseng and nodule Panax notoginseng were 0.984 9 and 0.986 6, respectively. The quality prediction model was verified by 10-fold cross-validation method. The average error was 0.3348 g and 0.494 9 g for cone Panax notoginseng and nodule Panax notoginseng, respectively.

关 键 词：三七主根 质量 分级 图像处理 预测模型 

分 类 号：TN911.73[电子电信—通信与信息系统]