农业机械学报2017，Vol.48Issue(4)：305-312,8.DOI:10.6041/j.issn.1000-1298.2017.04.040
基于高光谱成像的苹果品种快速鉴别
Rapid Identification of Apple Varieties Based on Hyperspectral Imaging
摘要
Abstract
In order to achieve rapid non-destructive identification of apple varieties,the methodology of near-infrared hyperspectral imaging on identification of apple varieties was investigated.Near infrared hyperspectral images with wavelength from 865 ～ 1 711 nm of total 90 sample fruits were collected from three different varieties ("Jonagold","Fuji" and "Qinguan" apples),and hyperspectral image area of the apple was selected as a region of interest (ROI).Reflection intensity data of the average reflex spectrum were extracted with resolution rate of 2.8 nm,then they were calculated with K-nearest neighbor (KNN) and the support vector machine (SVM) methods,respectively,which were checked with 5-fold cross-validation method.The results showed that the hyperspectral images of three varieties of apples all became clear within wavelength of 941 ～ 1 602 nm.Among ten distance-types' judgment of KNN with average reflection intensity at 200 wavelength-points,the identification accuracy of Chebychev,Euclidean and Minkowski reached the highest of 100％ when the parameter K was set at 3 or 5.While using the support vector machine-radial basis function (SVM-RBF) model,the accuracy rate reached above 92％ when the value of γ fell within 2-8 ～ 1.The highest recognition rate of this model reached 96.67％ when γ was set at 2-5 and C took the value of 16 amd 32 at the same time.The results demonstrated that nearinfrared hyperspectral imaging in combination with KNN was excellent and reliable for the rapid identification of apple varieties.This method could provide reference for identifying apple varieties in production.关键词
苹果/品种鉴别/高光谱成像/K近邻法/支持向量机Key words
apple/variety identification/hyperspectral image/K-nearest neighbor method/support vector machine分类
农业科技引用本文复制引用
马惠玲,王若琳,蔡骋,王栋..基于高光谱成像的苹果品种快速鉴别[J].农业机械学报,2017,48(4):305-312,8.基金项目
陕西省农业科技创新与攻关项目(2015NY023)和农业部现代苹果产业技术体系项目(CARS-28) （2015NY023）
