基于高光谱成像的猕猴桃表面疤痕无损识别

doi:10.3969/j.issn.1004-1524.2019.08.19

浙江农业学报 ›› 2019, Vol. 31 ›› Issue (8): 1372-1378.DOI: 10.3969/j.issn.1004-1524.2019.08.19

基于高光谱成像的猕猴桃表面疤痕无损识别

孟庆龙^1,2, 张艳^2,*, 尚静^1,2

1.贵阳学院食品与制药工程学院,贵州贵阳 550005;
2.贵阳学院农产品无损检测工程研究中心,贵州贵阳 550005

收稿日期:2019-01-12 出版日期:2019-08-25 发布日期:2019-08-30
通讯作者: 张艳,E-mail: Eileen_zy001@sohu.com
作者简介:孟庆龙(1989-),男,河北张家口人,博士,讲师,研究方向为农产品无损检测。E-mail: scumql@163.com
基金资助:
贵州省科技厅联合基金(黔科合LH字〔2014〕7174号); 贵州省科技计划(黔科合基础〔2019〕1010号); 贵州省普通高等学校工程研究中心(黔教合KY字〔2016〕017); 贵阳市财政支持贵阳学院学科与硕士点建设项目(SY-2019); 贵州省教育厅自然科学研究项目(〔2013〕199号)

Nondestructive recognition of surface defect on kiwifruits using hyperspectral imaging technology

MENG Qinglong^1,2, ZHANG Yan^2,*, SHANG Jing^1,2

1. Food and Pharmaceutical Engineering Institute, Guiyang University, Guiyang 550005, China;
2. The Research Center of Nondestructive Testing for Agricultural Products, Guiyang University, Guiyang 550005, China

Received:2019-01-12 Online:2019-08-25 Published:2019-08-30

摘要/Abstract

摘要： 为实现猕猴桃表面疤痕的快速无损识别,以贵长猕猴桃为研究对象,采用高光谱图像采集系统(400~1 000 nm)采集完好无损猕猴桃和表面有疤痕猕猴桃的高光谱图像。对采集到的高光谱图像进行了最小噪声分离变换,结合阈值分割及数学形态学处理方法提出了猕猴桃表面疤痕的识别方法。结果表明:采用最小噪声分离变换可有效地消除高光谱图像中的噪声;完好无损和表面有疤痕的猕猴桃样本在700~810 nm以及810~1 000 nm的光谱反射率值具有明显的差异,选取785.98 nm处的光谱反射率值为0.30~0.56以及982.59 nm处的光谱反射率值为0.54~0.73作为区分猕猴桃正常区域和表面疤痕区域的阈值条件,进一步利用阈值分割方法对60个完好无损的和60个表面有疤痕的猕猴桃进行识别,正确识别率分别为98.3%和95.0%,说明高光谱成像技术可用于猕猴桃表面疤痕的快速无损识别。

关键词: 猕猴桃, 表面疤痕, 高光谱成像, 无损识别

Abstract: In order to explore a rapid and nondestructive recognition method to identify kiwifruits with surface defect, the hyperspectral imaging system was used to collect the hyperspectral image of Guichang kiwifruits with no defect and surface defect. After the minimum noise fraction transform (MNF), threshold segmentation and mathematical morphology, the recognition algorithm for kiwifruits with surface defect was proposed and then applied in 60 intact kiwifruits and 60 kiwifruits with surface defect. The results showed that the noise of the hyperspectral image of kiwifruits could be effectively removed by MNF transform. And the intact kiwifruits and kiwifruits with surface defect had obvious reflectance value between 700-810 nm and 810-1 000 nm. The spectral reflectance between 0.30-0.56 at 785.98 nm and the spectral reflectance between 0.54-0.73 at 982.59 nm were both selected as the threshold conditions to distinguish the region of normal and surface defect on kiwifruits. The correct identification rates for the intact kiwifruits and kiwifruits with surface defect reached 98.3% and 95.0%, respectively, which indicated that the hyperspectral imaging technology could be effective for identifying kiwifruits with surface defect.

Key words: kiwifruit, surface defect, hyperspectral imaging, nondestructive recognition

中图分类号:

S127

孟庆龙, 张艳, 尚静. 基于高光谱成像的猕猴桃表面疤痕无损识别[J]. 浙江农业学报, 2019, 31(8): 1372-1378.

MENG Qinglong, ZHANG Yan, SHANG Jing. Nondestructive recognition of surface defect on kiwifruits using hyperspectral imaging technology[J]. , 2019, 31(8): 1372-1378.

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基于高光谱成像的猕猴桃表面疤痕无损识别

Nondestructive recognition of surface defect on kiwifruits using hyperspectral imaging technology

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