基于机器视觉的茶小绿叶蝉识别方法研究

Recognition of Empoasca Flavescens Based on Machine Vision

将机器视觉技术引入到了茶小绿叶蝉的自动识别领域,以实现茶园中茶小绿叶蝉的准确及时预报。采集了自然场景下黄色诱虫板的图像,利用超像素分割算法和多DBSCAN聚类图像融合的方法对采集的图像进行区域分割,保证了目标区域的准确性和完整性。在此基础上,提取了目标图像子区域的L、a、b均值和标准差特征,构建了最小二乘支持向量机(LSSVM)自动识别模型。为解决训练样本中茶小绿叶蝉和其他害虫数量不均衡带来的分类超平面偏移问题,采用改进SMOTE算法和KS算法来提高模型对茶小绿叶蝉小样本的识别精度。结果表明,该算法的整体识别精度可达到99.03%,茶小绿叶蝉的查准率可达91.76%,为茶小绿叶蝉的实时检测提供了有效途径。

The machine vision technique is introduced to recognize the Empoasca flavescens automatically on the yellow sticky traps in natural scenes in order to realize the accurate and timely forecast of Empoasca flavescens in tea garden. The superpixel segmentation algorithm and DBSCAN （density-based spatial clustering of applications with noise） cluster algorithm are employed to separate the interesting target regions from background, which ensures the accuracy and completeness of the target area. Then, six classification features, including mean value of L, a, and b and their standard deviation, are extracted from the marked area in target image. Last, LSSVM （least squares support vector machine） is developed to identify Empoasca flavescens from other insects that are captured by sticky traps. As the imbalanced sample number between Empoasca flavescens and other insects results in the low classification accuracy, the improved SMOTE （synthetic minority over-sampling technique） algorithm and KS （Kennard-Stone） algorithm are used to improve recognition accuracy of Emposace flavescens. The proposed algorithm achieves 99. 03% of the overall recognition accuracy, and the identification accuracy of Empoasca flavescens reaches 91. 76%. The proposed algorithm can provide an effective way for real-time detection of Empoasca flavescens.