Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane was converted into HSV color image. Then, the S-component image in the HSV image was extracted, and the S-component image was binarized by the Otsu algorithm to obtain the binary image, and then the binary image was processed by morphological closed operation, which can eliminate the noise and fill holes of binary image. Subsequently, the sugarcane area was segmented as the region of interest through the horizontal projection of the binary image, which lowered the amount of calculation while reducing interference. Finally, the vertical projection function of the binary image of the region of interest was established, and the function was continuously derived to obtain the minimum points, and then the position of sugarcane nodes was preliminarily determined by the obtained minimum points. Then, the final position of the sugarcane nodes were determined according to the number of nodes to be identified and the sum of pixels of each 5 columns on both sides of the minimum points. The pixel column positions corresponding to the minima of the sum are the accurate nodes positions determined by the proposed algorithm. The experimental results show that the algorithm proposed in this paper has a single node identification rate of 100%, with an average time consumption of 0.15 s, and a position deviation of less than 0.34 mm; a double nodes identification rate of 98.5%, with an average time consumption of 0.21 s, and a position deviation of less than 0.42 mm. Compared with other nodes identification algorithms mentioned in this paper, it has higher identification rate and accuracy.

针对甘蔗种子自动切割中的甘蔗节点识别和定位困难，基于机器视觉系统，提出了一种基于垂直投影函数最小点局部像素总和的甘蔗节点识别算法。首先，根据黄甘蔗的颜色和质地特征，将捕获的甘蔗的RGB图像转换为HSV彩色图像。然后，提取HSV图像中的S分量图像，并通过Otsu算法对S分量图像进行二值化处理以获得二值图像，然后通过形态学封闭运算对二值图像进行处理，从而消除了噪声和填充二进制图像的孔。随后，通过二值图像的水平投影，将甘蔗区域分割为目标区域，这降低了计算量，同时减少了干扰。最后，建立感兴趣区域二值图像的垂直投影函数，并连续导出该函数以获得最小点，然后根据所获得的最小点初步确定甘蔗节点的位置。然后，根据要识别的节点数和最小点两侧每5列像素的总和，确定甘蔗节点的最终位置。与总和的最小值相对应的像素列位置是由所提出的算法确定的精确节点位置。实验结果表明，该算法的单节点识别率为100％，平均耗时为0.15 s，位置偏差小于0.34mm；双节点识别率为98.5％，平均耗时为0.21 s，位置偏差小于0.42 mm。与本文提到的其他节点识别算法相比，它具有更高的识别率和准确性。