基于优化DBSCAN算法的玉米种子纯度识别

摘要

In order to identify maize purity rapidly and efficiently, the image processing technology and clustering algorithm were studied according to the maize seed and characteristics of the seed images. An improved DBSCAN on the basis of farthest first traversal algorithm ( FFT) adapting to maize seeds purity identification was proposed. The color features parameters of the RGB, HIS and Lab color models of maize crown core area were extracted. H, S and B were selected to be the effective characteristic vector. The abnormal points of different density characteristic vector points were separated by FFT. Then clustering results were combined after local density cluster by DBSCAN. Experiment results showed that the method played a great role in improving the accuracy of maize purity identification.%为快速有效地识别玉米种子纯度,针对玉米种子图像特征,对玉米种子的图像处理方法和聚类算法进行研究,提出一种基于最远优先遍历的DBSCAN玉米种子纯度识别算法.该方法首先提取玉米种子冠部核心区域的RGB、HIS和Lab种颜色模型特征参数,选取H、S、B作为识别向量；其次通过最远优先遍历算法剔除密度差异特征向量边缘异常散点；最后采用DBSCAN算法进行密度聚类.实验结果表明,该方法玉米纯度识别正确率达93.3％.