文献类型：期刊文献

英文题名：Method of image detection for ear of corn based on computer vision

作者：Liu, Changqing[1]; Chen, Bingqi[2]

第一作者：刘长青

通讯作者：Chen, B.

机构：[1] College of Mechanical and Electrical Engineering, Beijing Union University, Beijing 100020, China; [2] College of Engineering, China Agricultural University, Beijing 100083, China

第一机构：北京联合大学机器人学院

通讯机构：[2]College of Engineering, China Agricultural University, Beijing 100083, China

年份：2014

卷号：30

期号：6

起止页码：131-138

外文期刊名：Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

收录：EI(收录号：20141717605594);Scopus(收录号：2-s2.0-84898620341)

语种：英文

外文关键词：Cameras - Data acquisition - Grain (agricultural product) - Graphic methods - Image processing - Pixels - Stepping motors

摘要：The parameters such as the length, the number of ear rows, and the quantity of kernels in an ear of corn were measured during corn breeding and quality studies. It is usually done mainly manually. This research proposes an efficient image processing algorithm to detect the parameters of an ear of corn based on a machine vision. An experimental device was designed to detect the parameters. It mainly included a computer, a module of data acquisition and control, a stepper motor, a stepper motor driver, a PC camera, and other mechanical components. The computer was used to control the stepper motor to rotate the ear of corn and trigger the PC camera to capture images. The image was segmented after the ear of corn was captured. Its contour was traced. The length and the width of it were obtained by measuring the contour. The horizontal and vertical accumulated pixel values histograms were used in this research. One point in the upper edge and one point in the lower edge of the central ear row were found by first searching for the concaves of the horizontal accumulated pixel values histogram in a specified region. All the points in the upper and the lower edges of the central row were obtained by searching for the concaves of the horizontal accumulated pixel values histograms in a specified moving region which moved following the edge of the central ear row direction. So the image of this central ear row was determined. Each gap between the adjacent kernels could be distinguished by searching for the concaves of the vertical accumulated pixel values histogram in the image area of the central ear row. Then the width of the central ear row and the quantity of kernels in this ear row were recorded. The image of the next adjacent ear row was taken while this ear row was rotated to the location in which the former ear row was imaged. The condition of stopping detection was judged by matching the image of the current ear row with the first. So the number of the ear rows was determined. The quantity of the kernels in this ear of corn could be obtained by accumulating the kernels of all ear rows. In this research, an experimental device was designed to detect the parameters of an ear of corn. And an algorithm was supplied on the base of a machine vision for the same purpose. The image of each ear row in the ear of corn was effectively taken with no repeat. The parameters were detected such as the length and the width of the ear of corn, the width of one ear row, the number of ear rows, and the quantity of kernels in the ear of corn. Experiments showed that the measurement accuracy of the length, the width, and the number of the ear rows of the ear of corn was up to 98%. The measurement accuracy of the width of each ear row and the quantity of kernels was up to 95%. The detection speed was about 102 seconds per ear of corn.