CAJ | 학술논문

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10cm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。
채집청명시기다협도상，획취다협적도상신식。논문수선분석료눈아여로협적G화G-B분량적안색신식，해안색신식차이능구유효구분눈아화배경；연후근거분석결과설정초시역치，이용개진적최대방차자동취역법계산G화G-B분량적분할역치；최후제출료다협눈아적식별산법。실험결과표명해산법능유효소제광선적영향，쾌속식별눈아；상궤여다수간적거리위10cm좌우시，식별준학솔위92%。본연구적방법화결과가위다협지능채적궤기적연발제공기술지지。
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 10 cm. The research results were useful to provide recognition technical supports for an intelligent picking machine development.