CAJ | 학술논문

针对马铃薯内外部缺陷多项指标难以同时识别的问题，提出了一种半透射高光谱成像技术采用流形学习降维算法与最小二乘支持向量机（LSSVM ）相结合的方法，该方法可同时识别马铃薯内外部缺陷的多项指标。试验以315个马铃薯样本为研究对象，分别采集合格、外部缺陷（发芽和绿皮）和内部缺陷（空心）马铃薯样本的半透射高光谱图像，同时为了符合生产实际，将外部缺陷马铃薯的缺陷部位以正对、侧对和背对采集探头的随机放置方式进行高光谱图像采集。提取马铃薯样本高光谱图像的平均光谱（390～1040 nm ）进行光谱预处理，然后分别采用有监督局部线性嵌入（SLLE）、局部线性嵌入（LLE）和等距映射（Isomap）三种流形学习算法对预处理光谱进行降维，并分别建立基于纠错输出编码的最小二乘支持向量机（ECOC‐LSS‐VM ）多分类模型。通过分析和比较建模结果，确定SLLE为最优降维算法，SLLE‐LSSVM 为最优马铃薯内外部缺陷识别模型，该方法对测试集合格、发芽、绿皮和空心马铃薯样本的识别率分别达到96.83％，86.96％，86.96％和95％，混合识别率达到93.02％。试验结果表明：基于半透射高光谱成像技术结合SLLE‐LSSVM的定性分析方法能够同时识别马铃薯内外部缺陷的多项指标，为马铃薯内外部缺陷的快速在线无损检测提供了技术参考。
침대마령서내외부결함다항지표난이동시식별적문제，제출료일충반투사고광보성상기술채용류형학습강유산법여최소이승지지향량궤（LSSVM ）상결합적방법，해방법가동시식별마령서내외부결함적다항지표。시험이315개마령서양본위연구대상，분별채집합격、외부결함（발아화록피）화내부결함（공심）마령서양본적반투사고광보도상，동시위료부합생산실제，장외부결함마령서적결함부위이정대、측대화배대채집탐두적수궤방치방식진행고광보도상채집。제취마령서양본고광보도상적평균광보（390～1040 nm ）진행광보예처리，연후분별채용유감독국부선성감입（SLLE）、국부선성감입（LLE）화등거영사（Isomap）삼충류형학습산법대예처리광보진행강유，병분별건립기우규착수출편마적최소이승지지향량궤（ECOC‐LSS‐VM ）다분류모형。통과분석화비교건모결과，학정SLLE위최우강유산법，SLLE‐LSSVM 위최우마령서내외부결함식별모형，해방법대측시집합격、발아、록피화공심마령서양본적식별솔분별체도96.83％，86.96％，86.96％화95％，혼합식별솔체도93.02％。시험결과표명：기우반투사고광보성상기술결합SLLE‐LSSVM적정성분석방법능구동시식별마령서내외부결함적다항지표，위마령서내외부결함적쾌속재선무손검측제공료기술삼고。
The present paper put forward a non‐destructive detection method which combines semi‐transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM ) to recognize internal and external defects in potatoes simultaneously .Three hundred fifteen potatoes were bought in farmers market as research object ,and semi‐transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes .In order to conform to the actual production ,defect part is randomly put right ,side and back to the acquisition probe when the hyperspectral images of ex‐ternal defects potatoes are acquired .The average spectrums (390~1 040 nm) were extracted from the region of interests for spectral preprocessing .Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data ,including supervised locally linear embedding (SLLE) ,locally linear embedding (LLE) and isometric mapping (ISOMAP) ,the low‐dimensional data gotten by manifold learning algorithms is used as model input ,Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi‐target classification model .By comparing and analyzing results of the three models ,we concluded that SLLE is the optimal manifold learning dimension reduction algorithm ,and the SLLE‐LSS‐VM model is determined to get the best recognition rate for recognizing internal and external defects potatoes .For test set data , the single recognition rate of normal ,bud ,green rind and hollow heart potato reached 96.83% ,86.96% ,86.96% and 95% re‐spectively ,and he hybrid recognition rate was 93.02% .The results indicate that combining the semi‐transmission hyperspectral imaging technology with SLLE‐LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on‐line non‐destructive detecting of the internal and external defects potatoes .