Abstract: Crop seeds are the basis of agricultural production. Seed testing, as an important tool, plays an indispensable role in all aspects of seed production, trade, and utilization. However, traditional crop seed identification methods are inefficient and require the support of manpower as well as specialized testing equipment. In contrast, machine vision technology can realize non-destructive detection of targets by simulating human visual function with high efficiency and accuracy, which helps to realize the automation and intelligence of variety identification, grading, and classification of crop seeds. The paper first briefly describes the method of image acquisition and pre-processing in machine vision technology, and gives the current mainstream processing flow by taking corn seeds as an example, then specifically describes the application of the two detection methods of traditional machine learning and deep learning in machine vision technology in the detection of crop seeds. Finally, for the research on corn unsound kernels, while dividing it into the above two detection methods, this paper gives a specific description, and also points out the current problems and the future research direction of corn unsound kernel detection.

摘要： 农作物种子是农业生产的基础。种子检测作为一种重要的手段，在种子生产、贸易和利用的各个环节都扮演着不可或缺的角色。然而传统的农作物种子识别方法效率低，需要人力以及专业检测设备的支持。相比之下，机器视觉技术能够通过模拟人的视觉功能来实现对目标的无损检测，效率高、准确度高，有助于实现农作物种子的品种识别、分级、分类的自动化、智能化。首先简单叙述了机器视觉技术中图像采集、预处理的方法，并以玉米种子为例给出了目前主流的处理流程，然后具体叙述了机器视觉技术中传统机器学习和深度学习两种检测方式在农作物种子检测中的应用，最后针对玉米不完善粒的研究，在分为以上两种检测方式进行具体叙述的同时，指出了目前存在的问题以及玉米不完善粒检测未来的研究方向。