采集清明时期茶叶图像,获取茶叶的图像信息。论文首先分析了嫩芽与老叶的G和G-B分量的颜色信息,该颜色信息差异能够有效区分嫩芽和背景;然后根据分析结果设定初始阈值,利用改进的最大方差自动取阈法计算G和G-B分量的分割阈值;最后提出了茶叶嫩芽的识别算法。实验结果表明该算法能有效消除光线的影响,快速识别嫩芽;相机与茶树间的距离为10βcm左右时,识别准确率为92%。本研究的方法和结果可为茶叶智能采摘机器的研发提供技术支持。

The images of tea leaf in Qingming period were taken by digital camera for extracting tea color information. Firstly, the color information of G and G-B component of tender leaf and old leaf based on RGB color model was analyzed. The color difference between tender leaf and old leaf was able to distinguish the tender leaf from background. Then the original threshold from color analysis was set and the segmentation thresholds for G and G-B component were calculated by improved Ostu (the algorithm of threshold automatically extracted according to the maximum deviation). Finally, the recognition algorithm of tea tender leaf was proposed. Experimental results showed that the algorithm is useful for eliminating the impact of light and rapid in identifying the tender leaf from background. The accuracy rate is more than 92% when the distance between camera and tea bush is about 10βcm. The research results were useful to provide recognition technical supports for an intelligent picking machine development.