摘要
为提高柑橘果面缺陷机器识别的正确率及缺陷等级的分级精度,研究柑橘病虫害疤痕的计算机视觉检测。对柑橘图像设置蓝色分量阈值去除背景,统计44幅有病虫害疤痕的柑橘图像中疤痕的亮度值,以此经验值作为亮度分段阈值,提取病虫害疤痕,并对病虫害疤痕进行逐行扫描连通,形成连通的病虫害疤痕区域,对该区域进行离散傅里叶变换,取其前4个谐波分量区分病虫害疤痕与果萼、果梗和花萼。44幅柑橘图像中疤痕的正确识别率为88.64%。试验结果表明:该方法能将柑橘病虫害疤痕进行识别与分级。
In order to improve accuracy of defect identification and defect grading of citrus fruit pericarp, a computer vision system to detect scars caused by disease and insect pest is investigated. Backgrounds of fruit images are removed with blue component threshold. Luminance statistic of 44 sample images of fruits with disease and insect pest is used as luminance threshold value to extract scar feature. Images with scar are non-interlaced scanned to form connected region. Discrete Fourier transform is applied to region, and first four harmonic components are used to identify scar of disease and insect pest, fruit calyx, peduncle and flower calyx. Correctness rate of scar identification of 44 citrus fruit images is 88.64%. Results show this system can identify and grade fruit defection caused by disease and insect pest.
出处
《食品与机械》
CSCD
北大核心
2009年第3期78-81,共4页
Food and Machinery
基金
湖南省自然科学基金项目(编号:2007JJ6129)
湖南省农业厅重点课题项目(编号:200704A)
湖南省教育厅科学研究项目(编号:06D059)
关键词
柑橘
病虫害疤痕
计算机视觉
识别
Citrus fruits
Scar of disease and insect pest
Computer vision
Identification
