基于机器视觉的嫁接幼苗特征识别方法

幼苗子叶方向的正确识别是瓜科嫁接机实现全程自动化的关键技术之一。该文研究了在自然光照条件下，对白籽南瓜砧木子叶方向特征的识别方法。首先对采集的幼苗图像进行预处理，提取出子叶边界；然后利用区域标记，依次提取目标幼苗子叶边界的最小外接矩形，在外接矩形内对幼苗边界进行椭圆形Hough变换，拟合两片子叶的轮廓曲线；最后依据子叶轮廓的椭圆形数学模型，求取幼苗子叶方向，幼苗生长点位置以及子叶叶片面积等特征信息。对100幅幼苗图像进行识别试验，成功率为85%。该文提出的方法可对嫁接南瓜幼苗的子叶方向特征进行有效的识别，并且通过调整参数，可用于其他幼苗的特征识别研究。 

Recognition Method of Grafted Seedling Characteristics Based on Machine Vision

Correct identification of direction of seedling cotyledon was one of critical technologies for grafting machine to achieve fully automated.White pumpkin seed stock cotyledon direction feature recognition method under natural light conditions was analyzed.Firstly，images of seedlings were pretreated to extract cotyledon boundary.Then minimum bounding rectangle of seedling cotyledons boundary was extracted successively using regional mark.Seedlings boundary was transformed through regularized Hough transform in the external rectangle to fit profile curve of two cotyledons.Finally，an ellipse mathematical model was established according to outline of cotyledon to strike direction of seedling cotyledons，to get seedling growing point position and to access to information of cotyledon leaf area and other features.100 seedling images were identified，reaching 85% success rate.The proposed method can identify direction characteristics of white pumpkin cotyledon effectively and can be applied to feature recognition of other varieties by adjusting parameters.