柑橘真菌感染部位的高光谱成像快速检测  

Nondestructive Detecting Rottenness Defect of Citrus By Using Hyper-Spectra Imaging Technology

作　　者：楚秉泉[1] 章海亮[1,2] 罗微[2] 何勇[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310058 [2]华东交通大学,江西南昌330013

出　　处：《光谱学与光谱分析》2017年第8期2551-2555,共5页Spectroscopy and Spectral Analysis

基　　金：国家重大仪器设备开发专项(2014YQ470377);国家支撑技术项目(2015BAD19B03);国家自然科学基金项目(61071220);江西省科技支持项目(20123BDH80014)资助

摘　　要：真菌感染是柑橘的一种常见病害,是柑橘腐烂的主要因素,自动化检测出柑橘真菌感染可以有效提高柑橘的商品价值和市场竞争力。运用高光谱成像技术对真菌感染柑橘腐烂部位的缺陷特征进行了快速识别检测。基于ROI提取柑橘真菌感染光谱曲线,对光谱矩阵进行主成分分析,分析权重曲线后得到4个特征波段,分别为615,680,710和725nm,然后对这4波段组合分别做主成分分析,通过分析权重曲线提取到615和680nm两个特征波段,基于这两个特征波段做主成分分析,以第2主成分图像为基础识别柑橘真菌感染部位,识别率达到了100%。高光谱成像技术可用于快速检测柑橘真菌感染引起的腐烂缺陷,为开发水果分级和缺陷检测等相关仪器设备的研究提供了理论方法和依据。Rottenness is a prevalent and devastating disease that threats citrus fruit.Automatic detection of rottenness can enhance the competitiveness and profitability of the citrus industry.In this study,hyper-spectral image technology was used nondestructively to detect citrus rottenness.Spectral curve in defects peel region of interest was analyzed and combined with principal component analysis to extract the four best bands.Principal component was used based on four best bands： 615 nm and 680 nm,710 nm and 725 nm peaks combination respectively and ultimately selected component（PC-2）as image classification and recognition obtained from the 615 nm and 680 nm principal component analysis and identification rate was 100%with a simple threshold segmentation.These results showed that using hyper-spectral as a kind of detection methods could be used for the evaluation of citrus rotteness recognition.

关 键 词：高光谱成像 柑橘 腐烂缺陷 主成分分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]