In response to the lack of equipment for targeted sowing of rootstocks for melons, as well as issues such as low efficiency in manual sowing and poor seedling quality, a method based on machine vision for identifying the geometry characteristics of white-seeded pumpkin seeds is proposed. Firstly, Jingxin No.2 rootstocks pumpkin seeds are selected as the research object, and industrial cameras are used to capture the original images of the seeds. The grayscale value, profile and morphological features of the seeds are obtained through grayscale transformation, mean filtering, and dynamic threshold processing. The best ellipse is fitted based on the direction and aspect ratio of the contour area, and the geometric center coordinates and long and short axis data of the seeds are obtained. The direction of the bud spots is determined by the maximum distance between the contour endpoints along the long axis and the geometric center point. Then, using trigonometric functions with the geometric center point as the reference, calculate the location of the bud spots, followed by experimental verification. The results of the experiment show that this method can effectively identify the position and angle information of the germination point, with an identification accuracy of 98.85%, an average angular deviation of 1.53°, and an average recognition time of 0.092 seconds for 25 seeds.

针对瓜类砧木定向播种装备缺乏，人工播种效率低、育苗质量差等问题，提出基于机器视觉的白籽南瓜种子几何特征的识别方法。选取京欣砧2号南瓜种子为研究对象，利用工业相机采集种子的原始图像，通过灰度化、均值滤波、动态阈值处理得到种子的灰度值、种子轮廓等形态特征，根据轮廓区域的方向和纵横比拟合最佳椭圆，并获得种子几何中心坐标和长短轴数据，以沿种子长轴方向的轮廓端点与几何中心点的最大距离来判断芽点方向，再以几何中心点为基准运用三角函数计算种子芽点所在位置，并开展试验验证。试验结果表明，利用该方法可以有效地判断出芽点位置和角度信息，芽点识别准确率为98.85%，芽点角度平均偏差为1.53°，25粒种子识别平均耗时为0.092 s。