Characterization of the Influence of Moisture Content on the Morphological Features of Single Wheat Kernels Using Machine Vision
Characterization of the Influence of Moisture Content on the Morphological Features of Single Wheat Kernels Using Machine Vision
复制标题
利用机器视觉表征水分含量对单粒麦粒形态特征的影响
DOI：
10.13031/2013.37062
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
N. White
中科院分区：
文献类型：
--
作者：
G. Ramalingam;Suresh Neethirajan;D. Jayas;N. White
本研究的目的是量化的形态特征的变化所造成的水分增加，使用机器视觉系统的加拿大西部小麦类的内核。100个单一的小麦籽粒为每个八个加拿大西部小麦类连续调节从12%至20%（湿基）的水分含量使用氢氧化钾（KOH）的浓度，调节相对湿度。使用具有行间转移电荷耦合器件（CCD）图像传感器的7.4× 7.4-µm像素分辨率的数码相机来获取单个籽粒的图像。马尼托巴大学加拿大小麦委员会谷物储藏研究中心开发的机器视觉算法实现了从小麦籽粒图像中提取7个形态特征（面积、周长、长轴长度、短轴长度、最大半径、最小半径和平均半径）。当水分含量从12%增加到20%时，加拿大西部红春小麦、加拿大西部琥珀硬粒小麦、加拿大草原春小麦白色小麦、加拿大草原春小麦、加拿大西部特强小麦、加拿大西部红冬小麦、加拿大西部硬白色春小麦和加拿大西部软白色春小麦籽粒的所有七个特征都显著（a = 0.05）不同。所有七个特征都表现出随含水量增加而线性增加的趋势。
The objective of this study was to quantify changes in morphological features of kernels of western Canadian wheat classes caused by moisture increase using a machine vision system. One hundred single wheat kernels for each of eight western Canadian wheat classes were successively conditioned from 12% to 20% (wet basis) moisture contents using potassium hydroxide (KOH) concentrations which regulated relative humidity. A digital camera of 7.4× 7.4-µm pixel resolution with an inter-line transfer charge-coupled device (CCD) image sensor was used to acquire images of single kernels. A machine vision algorithm developed at the Canadian Wheat Board Centre for Grain Storage Research, University of Manitoba, was implemented to extract seven morphological features (area, perimeter, major axis length, minor axis length, maximum radius, minimum radius, and mean radius) from the wheat kernel images. All the seven features of Canada Western Red Spring, Canada Western Amber Durum, Canada Prairie Spring White, Canada Prairie Spring Red, Canada Western Extra Strong, Canada Western Red Winter, Canada Western Hard White Spring, and Canada Western Soft White Spring wheat kernels were significantly (a = 0.05) different as the moisture content increased from 12% to 20%. All seven features showed a linearly increasing trend with an increase in moisture content.