In the Panax notoginseng quality intelligent management system, the big roots and fibrous roots cannot be cut automatically because the machine cannot distinguish the taproot, big roots, and fibrous roots of Panax notoginseng, resulting in the automatic cutting mechanism unable to obtain the control trajectory coordinate reference of the tool feed. To solve this problem, this paper proposes a visual optimal network model detection method, which uses the image detection method of marking anchor frames to improve the detection accuracy. A variety of deep learning network models are modified by the TensorFlow framework, and the best training model is optimized by comparing the results of training, testing, and verification data. This model is used to automatically identify the taproots and provide the control trajectory coordinate reference for the actuator that cuts big roots and fibrous roots automatically. The experimental results show that the optimal network model studied in this paper is effective and accurate in identifying the taproots of Panax notoginseng.

中文翻译：

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三七质量智能管理系统主根识别技术研究

在三七质量智能管理系统中，由于机器无法区分三七的主根、大根、须根，所以无法自动切断大根和须根，导致自动切削机构无法获取刀具进给的控制轨迹坐标参考。针对这一问题，本文提出了一种视觉最优网络模型检测方法，利用标记锚框的图像检测方法来提高检测精度。通过TensorFlow框架修改多种深度学习网络模型，通过对比训练、测试、验证数据的结果，优化最佳训练模型。该模型用于自动识别主根，为自动切割大根和须根的执行器提供控制轨迹坐标参考。实验结果表明，本文研究的最优网络模型能够有效、准确地识别主根。三七。