基于机器视觉的乘用式智能采茶机设计与试验

Design and Experiment of Intelligentized Tea-plucking Machine for Human Riding Based on Machine Vision

针对目前乘用式采茶机作业时对采摘面的茶芽不能识别大小、老嫩茶叶一刀切下的弊端,设计了一种基于机器视觉的乘用式采茶机,提出了嫩茶自动识别与采茶机割刀的自动调平调高控制方法。通过对采茶机割台的位置伺服和水平度伺服控制,使得割刀面与茶陇蓬面有一个较好吻合,并能将割台与大地水平面保持一致;为了实现更为精准地切割,在采摘面的茶芽识别时采用2次最大类间差分法。首先获取采摘面的图像,利用B分量的阈值分割出茶叶区域;然后选取G和G-B分量的阈值,从茶叶区域中再分割出嫩茶区域;最后计算采摘面上嫩茶部分所占面积比例,以70%作为视觉伺服的控制基准。试验研究表明,提出的基于机器视觉的乘用式采茶机的嫩茶自动识别与采茶机割刀的自动调平调高控制方法能有效解决目前机采茶叶老嫩一刀切下的弊端,为今后全自动化茶叶采摘奠定了基础。

Presently,tea-plucking machine has a disadvantage that it cuts indiscriminately without identification of the tender tea. In order to solve this problem,a kind of tea-plucking machine was designed based on machine vision. A method was put forward to cut intelligently fused with position servo,visual servo and levelness servo. The cutting line was kept consistently with tea ridge and the header of machine was consistent with horizontal plane by levelness servo. The initial height of the cutter was set by position servo. In order to make the cutting more precise,PID algorithm was used to obtain highly subtle measurements. In terms of visual servo inspection,firstly,tea images of picking surface were taken and the threshold of B component in RGB was used to eliminate background and segment the range of tea. Secondly,the thresholds of G and G-B components were analyzed to distinguish tender leaves from the image by improved OSTU（ the algorithm of threshold automatically extracted according to the maximum deviation）. Template matched method and threshold of R component were useful to identify cutter line. Finally,the proportion of tender leaves area above cutter line in the image was calculated and its height was adjusted to ensure the ratio above 70%. Experimental result shows that the proposed method solves present disadvantages of tea-plucking machine effectively. Also,the efficiency of picking was improved with reduced labor cost.