Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

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1 January, 018 Int J Agrc & Bol Eng Open Access at Vol. 11 No Modelng for mung bean varety classfcaton usng vsble and near-nfrared hyperspectral magng Chuanq Xe, Yong He * (College of Bosystems Engneerng and Food Scence, Zhejang Unversty, Hangzhou , Chna) Abstract: Ths study was carred out to nvestgate the feasblty of usng vsble and near nfrared hyperspectral magng for the varety classfcaton of mung bea. Raw hyperspectral mages of mung bea were acqured n the wavelengths of nm, and all mages were calbrated by the whte and dark reference mages. The spectral reflectance values were etracted from the regon of nterest (ROI) of each calbrated hyperspectral mage, and then they were treated as the ndependent varables. The dependent varables of four varetes of mung bea were set as 1,, 3 and 4, respectvely. The etreme learnng machne (ELM) model was establshed usng full spectral wavelengths for classfcaton. Modfed gram-schmdt (MGS) method was used to dentfy effectve wavelengths. Based on the selected wavelengths, the ELM and lnear dscrmnant analyss (LDA) models were bult. All models performed ecellently wth the correct classfcaton rates (CCRs) coverng 99.17%-99.58% n the tranng sets and 99.17%-100% n the testng sets. Ffteen wavelengths (43 nm, 455 nm, 468 nm, 560 nm, 705 nm, 736 nm, 760 nm, 841 nm, 861 nm, 91 nm, 930 nm, 937 nm, 938 nm, 959 nm and 965 nm) were recommended by MGS. The results demotrated that hyperspectral magng could be used as a non-destructve method to classfy mung bean varetes, and MGS was an effectve wavelength selecton method. Keywords: vsble and near-nfrared hyperspectral magng, mung bean, classfcaton, modelng, wavelength selecton DOI: /j.jabe Ctaton: Xe C Q, He Y. Modelng for mung bean varety classfcaton usng vsble and near-nfrared hyperspectral magng. Int J Agrc & Bol Eng, 018; 11(1): Introducton Mung bean s welcomed by many people n Chna, Korea, Burma, Inda, Japan, Thaland, Pakstan and other Southeast Asan countres due to ts hgh edbleness and medcnal value [1]. It conta a lot of nutrents and functonal compounds, such as proten, vtamn, lpd, phytochemcals and fber []. However, dfferent varetes of mung bea have dstnct agronomcal, processng and nutrtonal characterstcs [3]. Thus, dentfcaton of the mung bean varety s of great mportance. The current standard methods to dfferentate varetes of bea are manly based on artfcal see and bochemcal operaton [4,5]. However, these methods are neffcent, tme-coumng and destructve. Also, professonal and qualfed technca are requred for such detecton methods. Thus, an advanced method (hyperspectral magng) s needed. Ths study was carred out to dentfy the mung bean varety by usng the vsble and near nfrared hyperspectral magng. Based on the hyperspectral magng, a multspectral detecton system can be desgned by the effectve wavelengths, whch has the potental to be used n ndustry for onlne and non-destructve detecton. Hyperspectral magng combnes both spectral and magng Receved date: Accepted date: Bographes: Chuanq Xe, PhD, Post-doctoral Assocate, research nterests: crop dseases detecton usng spectral, hyperspectral magng and RGB technques, food engneerng, unmanned aeral vehcle, ntrogen nutrton of corn crops. Emal: cqe@zju.edu.cn. *Correspondng author: Yong He, PhD, Professor, research nterests: spectral, hyperspectral magng, unmanned aeral vehcle, nternet of thngs. 866 Yuhangtang Road, Hangzhou , Chna. Tel/Fa: , Emal: yhe@zju.edu.cn. technques together, and has been wdely used n prevous researches due to ts advantages such as beng fast, non-destructve, effectve and accurate [6-8]. Usng the hyperspectral magng system, a spatal map can be created when the sample s scanned by the camera. Each pel of the hyperspectral mage has a spectrum coverng the full spectral range. The hyperspectral mage (hyperspectral cube) s composed of a seres of mages at the whole wavelengths, and t conta both spectral and spatal nformaton [9]. It can provde a full database wth nternal and eternal features of the samples [10], and dfferent varetes of objectves may have varous eternal and nternal characterstcs, such as color, teture and nutrton content, drectly resultng n the spectral sgnature dfferently. The spectral sgnature from a certan pel of the mage s useful for the dscrmnaton and classfcaton of the objectves [11]. Ths technque has been appled n varety classfcaton n many prevous studes. The oat and groat kernels were classfed usng the near-nfrared (NIR) hyperspectral magng system coverng the wavelengths of nm [1]. The partal least squares-dscrmnant analyss (PLS-DA) model was bult for the classfcaton, and three effectve wavelengths were dentfed (113 nm, 1195 nm and 1608 nm). Kong et al. [10] dentfed four cultvars of rce seeds usng the NIR hyperspectral magng n the wavelengths of nm. PLS-DA, soft ndependent modelng of class analogy (SIMCA), K-Nearest neghbor (KNN), support vector machne (SVM) and random forest (RF) were establshed to dentfy the cultvars. Twelve useful wavelengths (1069 nm, 1079 nm, 1139 nm, 1167 nm, 1183 nm, 17 nm, 181 nm, 1304 nm, 138 nm, 1389 nm, 1467 nm and 1558 nm) were selected by weghted regresson coeffcents method. Kamruzzaman et al. [13] used the NIR hyperspectral magng to

2 188 January, 018 Int J Agrc & Bol Eng Open Access at Vol. 11 No.1 dscrmnate lamb muscles. Prncpal component analyss (PCA) was used to compress the dmeonalty, and s wavelengths (934 nm, 974 nm, 1074 nm, 1141 nm, 111 nm and 1308 nm) were determned by PCA loadngs. Qualty classfcaton of cooked and slced hams was also studed usng NIR hyperspectral magng [14]. PCA was used for selectng effectve wavelengths, and eght wavelengths (980 nm, 1061 nm, 1141 nm, 1174 nm, 115 nm, 135 nm, 1436 nm and 1641 nm) were dentfed to dscrmnate dfferent turkey ham qualtes. Barbn et al. [15] nvestgated the dentfcaton of fresh and frozen-thawed meat by usng the NIR hyperspectral magng. The PLS-DA model acheved the correct classfcaton of 100% based on the effectve wavelengths recommended by weghted regresson coeffcents method. We et al. [16] dscrmnated the rpeness (unrpe, md-rpe, rpe and over-rpe) of persmmon frut usng the vsble and near-nfrared hyperspectral magng ( nm). Three classfcaton models (lnear dscrmnant analyss (LDA), SIMCA and least squares support vector machnes (LS-SVM)) were bult to classfy the dfferent types of persmmo. Three wavelengths (518 nm, 711 nm and 980 nm) were selected by successve projecton algorthm (SPA). Based on the spectral and teture feature at the three wavelengths, the LDA model obtaned the best correct classfcaton rate of 95.30%. Deng et al. [17] used spectral angle mappng (SAM) model combned wth hyperspectral mages to dstngush weeds from background and cabbages. All of these studes demotrated that the hyperspectral magng has the potental to be used for varety classfcaton. The am of ths study was to develop an effectve method to classfy mung bean varetes usng the hyperspectral magng. The specfc objectves were to: (1) classfy dfferent varetes of mung bea based on spectral reflectance nformaton; () dentfy effectve wavelengths that play the most sgnfcant roles for the varety classfcaton usng modfed gram-schmdt (MGS); and (3) compare the performance of dfferent classfcaton models. Materals and methods.1 Work flow The process of ths study can be seen n Fgure 1. In the frst step, raw hyperspectral mages of four varetes of mung bea were acqured by the vsble and near nfrared hyperspectral magng camera. All raw hyperspectral mages were then corrected by the whte and dark reference mages. Spectral reflectance values were etracted from the corrected hyperspectral mages and treated as the dependent varables. All samples were dvded nto two sets (tranng and testng) at a rato of :1. One classfcaton model (etreme learnng machne, ELM) was establshed based on the full spectral wavelengths. Then, MGS method was used to select effectve wavelengths. Based on these selected wavelengths, the ELM and LDA models were bult, respectvely. The optmal model was fnally determned by the values of correct classfcaton rates (CCRs).. Samples Four dfferent brands of mung bea (Jnlongyu, Ouy, Sawengfu and Xangmanyuan) were used n ths study. A volume of 60 cm 3 of mung bea were evenly dstrbuted n a glass dsh (d=90 mm). For each brand, there were 90 dshes. Each dsh represented one sample, and was then scanned ndvdually by the hyperspectral magng system. Fnally, a total of 360 samples for the four varetes were used n ths study. Fgure 1 Flow chart of ths study.3 Eperment devce A hyperspectral magng system whch covers the spectral wavelengths of nm was used n ths study (Fgure ). Ths system costed of a CCD camera (C , Hamamatsu Cty, Japan), a le (OLE-3, USA), an magng spectrograph (V10E, Specm, Oulu, Fnland), two lght sources (Orel Itruments, USA) provded by two 150 W quartz tungsten halogen lamps, a computer, and a conveyer belt operated by a stepper motor (IRCP0076, Isuzu Optcs Corp., Tawan, Chna). The area CCD array detector has (spatal spectral) pels, and the spectral resoluton s.8 nm. Samples were scanned by the hyperspectral magng system lne by lne, and the reflected lght was dspersed by the spectrograph and captured by the area CCD array detector n spatal-spectral aes. A hyperspectral mage can be generated when the sample s scanned by the camera. Each hyperspectral mage conta hundreds of gray mages, and each pel conta both spectral and spatal features (Fgure 3). The ENVI 4.7 software (Research System Inc., Boulder, Co., USA) was used for obtanng spectral reflectance nformaton from hyperspectral mages. MATLAB R009a (The Math Works Inc., Natck, MA, USA) was used to dentfy effectve wavelengths and establsh classfcaton models. Fgure Vsble and near-nfrared hyperspectral magng system

3 January, 018 Xe C Q, et al. Modelng for mung bean varety classfcaton usng vsble and near-nfrared hyperspectral magng Vol. 11 No Fgure 3 Hyperspectral magng of mung bea.4 Images acquston and correcton In order to get an mage wthout dstorton and overeposure, the hyperspectral magng camera should be tested several tmes for obtanng the best sutable eposure tme and movng speed. Fnally, the two parameters were 0.09 s and 3.0 mm/s, respectvely. A dark reference mage wth the reflectance of 0% was acqured by turnng off the lght and coverng the le wth ts cap. The whte reference mage wth the reflectance of about 99% was obtaned by scannng a whte Teflon board (CAL-tle00, 00 mm 5 mm 10 mm, Isuzu Optcs Corp., Tawan, Chna). Then, each glass dsh wth mung bea was put on the conveyer belt to be scanned by the hyperspectral magng system n sequence. A raw hyperspectral mage (hyperspectral cube) wth a dmeon of (, y, ) was created as the sample was scanned along the drecton of the y dmeon. The hyperspectral mage had 67 pels n the y dmeon and 51 bands n the dmeon. When the raw hyperspectral mage was generated, t should be corrected wth the dark and whte reference mages based on the Equaton (1): I r I c = I w I I where, I r s the raw hyperspectral mage; I d s the dark reference mage; I w s the whte reference mage, and I c s the corrected hyperspectral mage..5 Spectral reflectance etracton After mages acquston and correcton, an area (regon of nterest, ROI) wth pels was cropped from each corrected hyperspectral mage, and spectral reflectance values of all pels etracted from the ROI were averaged and treated as the ndependent varables. The dependent varables for the four varetes were set as 1 (Jnlongyu), (Ouy), 3 (Sawengfu) and 4 (Xangmanyuan), respectvely. After all samples were sorted accordng to the varety value, one sample was then randomly pcked out from every three ones [18], resultng n 40 samples for the tranng set and 10 ones for the testng set..6 Classfcaton models In order to compare the performance of dfferent models and smultaneously dentfy the optmal one, two classfcaton models were used n ths study. ELM, whch has a good generalzaton performance for feed forward neural networks, was appled n buldng classfcaton and regresson models n prevous studes [19,0]. It can effectvely solve problems such as local mnma and over-fttng [1]. The learnng speed of ELM s faster than that of tradtonal feed forward network such as the d d (1) back-propagaton (BP) algorthm []. LDA s a supervsed recognton algorthm, and can be used to fnd a lnear combnaton of features that can classfy dfferent varetes of objectves [16]. The LDA algorthm produces a number of orthogonal lnear dscrmnant functo whch makes the samples to be classfed n dfferent categores [3]. Both classfcaton models were bult usng MATLAB R009a software..7 Wavelength selecton The spectral feature, whch covers the wavelengths from 380 nm to 103 nm, usually conta hghly redundant nformaton among dfferent wavelengths [4]. Thus, the selecton of effectve wavelengths s a crucal step n spectral analyss [5]. The fnal am of selectng effectve wavelengths s to establsh a subset of spectral wavelengths to replace the whole ones. The selected wavelengths can be equal to or even more effectve than the full wavelengths [6]. Also, these wavelengths can not only smplfy the model but also be used to develop a multspectral magng classfcaton system. MGS was used to select useful wavelengths n ths study. The prncple of MGS s to buld a new orthogonal bass based on the orgnal one by projecton. An egenvector, whch s selected from the feature matr X=[ 1,,, n ], can be treated as the frst orthogonal bass z1. Then the projecton of other vector quanttes onto ths orthogonal bass can be calculated (, ) = z1 ( proj z1 z1 ). Makng the dfference value ( '= proj ) obtaned from other vector quanttes and ths projecton and z1 orthogonal, resultng n n-1 egenvectors of the frst orthogonal bass. Smlarly, the projecton of the class label vector Y onto the ( Y,z1) frst orthogonal bass ( Y proj = z1 ) can be calculated. Make z1 the dfference value obtaned from Y and projecton and z1 orthogonal. Fnally, the egenvector can be dentfed. The man procedures can be seen n Table 1 [7]. Step 1: Each egenvector of characterstc matr X=[ 1,,, n ] s normalzed, n (=1,, n); Step : The nner product of class label vector Y and each normalzed egenvector s calculated y proj_ =(Y n ) (=1,, n), the egenvector (s=1,, n) that mamzes y proj_ s selected as normalzed orthogonal bass; Step 3: The egenvector of feature matr X=[ 1,,, n ] s projected onto normalzed orthogonal bass,, = proj, the dfference value ( '= proj ) of egenvector and projecton s calculated; Step 4: The class label vector Y s projected onto normalzed orthogonal bass, Y, Y = proj, the dfference value (Y'= Y Y proj ) of Y and projecton s calculated; Step 5: X=[' 1, ' s-1, ' s+1,, ' n ], Y=Y', n=n 1; Step 6: Calculaton wll stop when the requred egenvectors are obtaned, or t wll return to step. Ths method belongs to the backward feature selecton, whch eecutes the selecton by removng one or more feature from the whole features. The backward feature selecton method can keep

4 190 January, 018 Int J Agrc & Bol Eng Open Access at Vol. 11 No.1 the nformatve features as many as possble. On the other hand, MGS coders the relatohp between the feature structures and label nformaton, whch can mprove the performance of the subsequent classfcaton or predcton model. As a whole, the merts of MGS can be summarzed as follows: (1) backward feature selecton, and () the feature selecton combnes the feature structure and label nformaton. Table 1 Input Output Man procedures of modfed gram-schmdt (MGS) algorthm Feature matr X=[ 1,,, n], class label vector Y feature matr Xs selected by MGS 1 for k=1:featnum %Man loop n %Normalzed feature matr = argma Y,, X k %Fndng the drecton of 7 end n s mamum nner product, %Projectng X onto, computng the resdual X Y, %Projectng Y onto, computng the resdual Y Y Y X=[' 1, ' s-1,' s+1,,' n], Y=Y', n=n 1 %Changng the varables to terate 8 return s 3 Results and dscusson 3.1 Spectral feature analyss The mean spectral reflectance curves of the four varetes of mung bea were shown n Fgure 4. It can be seen that the general trends of the spectral reflectance curves of dfferent varetes were very smlar wth some nose at the begnnng of the wavelength, and some peaks as well as valleys coverng the whole wavelengths. Dfferent peaks and valleys are correspondng to varous spectral bands, for eample, the wavelength at 970 nm ndcates there s a water absorpton band here. Also, obvous dfference can be seen n the spectral regon of nm, whch mea most of the effectve wavelengths for the classfcaton should be located n ths regon. In order to mnmze the nfluence of the nose, the wavelengths at the begnnng were rejected, resultng n spectral wavelengths of nm (band 14-band 51) beng used n ths study. Fgure 4 Average spectral reflectance curves of the four varetes of mung bea 3. Classfcaton based on whole wavelengths In ths study, the ELM classfcaton model was frstly establshed based on full spectral wavelengths. The reflectance values were treated as X varables, and the varetes were treated as Y varables (Jnlongyu-1, Ouy-, Sawengfu-3, Xangmanyuan-4). The ELM model obtaned a satsfyng result wth CCRs of 99.58% n the tranng set and 100% n the testng set. No samples were ncorrectly dentfed n the testng set. However, the large number of nput varables wll ncrease the calculaton tme and cannot be used for desgnng a multspectral classfcaton system. Therefore, effectve wavelengths should be dentfed for establshng smplfed classfcaton models. 3.3 Effectve wavelengths In order to mprove the performance of classfcaton models and smplfy the calculaton, MGS was carred out to dentfy the key wavelengths. Based on the selected wavelengths, the spectral data set was then reduced nto a matr wth a dmeon of m (m was the number of samples, and was the number of selected wavelengths). As a result, a total of ffteen wavelengths (43 nm, 455 nm, 468 nm, 560 nm, 705 nm, 736 nm, 760 nm, 841 nm, 861 nm, 91 nm, 930 nm, 937 nm, 938 nm, 959 nm and 965 nm) were obtaned. Compared wth full spectral wavelengths, the number of selected wavelengths was greatly reduced. It only accounted for 3.01% of the number of whole wavebands (band 14-band 51). These selected wavelengths were then used as the new nput varables for establshng classfcaton models. 3.4 Classfcaton based on selected wavelengths The ELM and LDA models were then bult usng the selected wavelengths suggested by MGS (Table ). MGS-ELM model performed ecellently wth the CCRs of 99.17% n both tranng and testng sets. MGS-LDA model acqured the CCRs of 99.17% n the tranng set and 98.33% n the testng set. Compared wth the full wavelengths-based ELM model, there was a lttle decrease of CCR n the testng set of MGS-ELM model. However, the result was stll ecellent (99.17%). Also, the number of nput varables were largely reduced, whch made the classfcaton model smple. It ndcated that these wavelengths selected by MGS were very useful for classfyng dfferent varetes of mung bea. As mentoned above, the spectral reflectance curves were obvously dfferent n the wavelengths of nm. Most of the selected wavelengths were located n ths regon. Ths mght be why the selected wavelengths-based models could also obtan good classfcaton results. Table Classfcaton model Classfcaton results by usng dfferent models Number of wavelengths No. Tranng set Mssed CCR a /% No. Testng set Mssed CCR a /% MGS-ELM MGS-LDA Note: CCR a : correct classfcaton rate. 4 Concluso Ths study demotrated that the vsble and NIR hyperspectral magng could be used to classfy the four dfferent varetes of mung bea. MGS was effcent for selectng useful wavelengths. The numbers of selected wavelengths suggested by MGS only accounted for 3.01% of that of the full wavelengths. The full wavelengths-based ELM model obtaned an ecellent result wth a very hgh CCR value (100%) n the testng set. Those models establshed based on selected wavelengths obtaned promnent results wth CCRs rangng from 98.33% to 99.17% n the testng sets. Although the results acqured by selected wavelengths were a lttle lower than that obtaned by full wavelengths, they are stll ecellent. Moreover, a large amount of the full wavelengths were

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