计算机应用

• 人工智能与仿真 •    下一篇

基于机器视觉的油菜籽计数系统开发与设计

彭顺正,岳延滨,冯恩英,李莉婕,孙长青,赵泽英   

  1. 贵州省农业科学院科技信息研究所
  • 收稿日期:2020-04-22 修回日期:2020-06-30 发布日期:2020-06-30 出版日期:2020-08-14
  • 通讯作者: 彭顺正

Development and design of rapeseed counting system based on machine vision

  • Received:2020-04-22 Revised:2020-06-30 Online:2020-06-30 Published:2020-08-14
  • Contact: shun zhengpeng

摘要: 油菜籽千粒重对于油菜育种、种子质量评定、产量估测具有重要意义。针对目前贵州主要依靠人工测定油菜籽千粒重周期长、工作量大等问题,基于VS2013和OpenCV开发了一套油菜籽粒计数系统。系统功能主要包括图像读取、RGB颜色特征分析、籽粒角点检测、籽粒孔洞检测、籽粒计数。并根据油菜籽外观颜色、几何形状、空间排布特性特征,提出一套油菜籽粒计数算法,并将计数算法集成封装嵌入籽粒计数系统。通过对系统各项功能调试测试,各项功能运行正常,达到预期设计目标;通过籽粒检测试验,结果表明:籽粒计数算法检测准确率达88%。系统的构建对于实现贵州油菜籽粒计数自动化具有一定的参考价值。

Abstract: 1000-kernel weight of rapeseed is of great significance for rape breeding, seed quality assessment and yield estimation. Aimed at the current problem that Guizhou mainly relies on manual measurement of rapeseed thousand-grain weight cycle and long workload, based on VS2013 and OpenCV, a rapeseed grain counting system has been developed. System functions mainly include image reading, RGB color feature analysis, grain corner detection, grain hole detection, and grain counting. Based on the appearance color, geometric shape and spatial arrangement characteristics of rapeseed, a set of rapeseed counting algorithm is proposed, and the counting algorithm is integrated into the seed counting system. Through debugged and tested the various functions of the system, the functions work normally and the expected design goals is achieved. The results of the grain detection test show that the detection accuracy of the grain counting algorithm reaches 88%. The construction of the system has certain reference value for the realization of Guizhou rapeseed seed counting automation.

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