ug R

Hyperspectral imagingPrincipal component analysisDefect detectionOrangesRatio images

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near-infrared (NIR) regions, or the second principal component images using two wavelengths (691

ducts ir a lonte fruase the

Since the color is the natural sense we use to make our rstevaluation of the quality of fruits, most of the inspection systemsuse this information to detect skin defects of fruit (Miller andDelwiche, 1991; Leemans et al., 1998; Mendoza and Aguilera,2004; Blasco et al., 2007b). In terms of citrus, Recce et al. (1998)identied orange defects based on gray value differences betweendefects and sound peel in R and G component images. A neuralnetwork classier on rotation invariant transformations was used

proach could detect different peel conditions, a complex imageprocessing algorithm affect the real-time application in packing-house processing line. Kim et al. (2009) used 14 color texture fea-tures of grapefruit peel diseases to classify defects based on HISmodel. Lpez-Garca et al. (2010) developed a citrus fruits surfacedetection algorithm, which was based on multivariate image anal-ysis strategy and principal component analysis approach. The suc-cess ratio for the detection of individual defect was 91.5%. Theclassication accuracy was acceptable. However, the complexityof the algorithm restrained the detection speed.

Corresponding author. Tel.: +86 0571 86971885.

Computers and Electronics in Agriculture 78 (2011) 3848

Contents lists availab

tr

elsE-mail address: xqrao@zju.edu.cn (X. Rao).tion. Fresh market fruits are graded into quality categories accord-ing to parameters such as size, color, shape and external defects(Leemans and Destain, 2004). The rst two quality criteria havebeen automated on current commercial graders, but fruits gradingaccording to the presence of defects is yet very challenging,although the most advanced machines are also capable of detect-ing blemishes (Aleixos et al., 2002; Leemans and Destain, 2004;Blasco et al., 2007a).

accuracy of over 90%. Leemans and Destain (2004) compareddefect segmentation results of three models that were generatedusing the RGB, RGI and RGBI. The RGBI model, which was foundto be the best, correctly recognized 100% of the stem-ends and99.1% of defects based on Bayesian classication model. In a recentstudy, Blasco et al. (2007b) reported the application of near-infrared, ultraviolet and uorescence computer vision systems todetect the external defects of citrus fruits. Although proposed ap-1. Introduction

Citrus is one of the major fruit production is over 23 million tons. Foindustry has attempted to automadecrease production costs and incre0168-1699/$ - see front matter 2011 Elsevier B.V. Adoi:10.1016/j.compag.2011.05.010and 769 nm) in VIS region gave better identication results under investigation. However, the stem-endswere easily confused with defective areas. In order to solve this problem, representative regions of inter-est (ROIs) reectance spectra of samples with different types of skin conditions were visually analyzed.The researches revealed that a two-band ratio (R875/R691) image could be used to differentiate stem-ends from defects effectively. Finally, the detection algorithm of defects was developed based on PCAand band ratio coupled with a simple thresholding method. For the investigated independent test sam-ples, accuracies of 91.5% and 93.7% with no false positives were achieved for both sets of selected wave-lengths using proposed method, respectively. The disadvantage of this algorithm is that it could notdiscriminate between different types of defects.

2011 Elsevier B.V. All rights reserved.

n China. Its annual pro-g time, the agricultureit sorting in order toquality of the produc-

to recognize the radial color variation of the stem region. Diazet al. (2004) observed abnormal color on table olive was relatedwith low values of R and G co-ordinates. Defects were then seg-mented using a Bayesian model. Three different algorithms wereapplied to classify the olives, and results showed that a neural net-work with a hidden layer was able to classify the olives with anKeywords:could potentially be used in an in-line multispectral imaging system. The third principal componentimages using six wavelengths (630, 691, 769, 786, 810 and 875 nm) in the visible spectral (VIS) andDetection of common defects on oranges

Jiangbo Li, Xiuqin Rao , Yibin YingCollege of Biosystems Engineering and Food Science, Zhejiang University, 388 Yuhangtan

a r t i c l e i n f o

Article history:Received 15 December 2010Received in revised form 27 May 2011Accepted 29 May 2011

a b s t r a c t

To detect various commoning reectance images fromwith insect damage, windicity, heterochromatic stripevaluated using principal c

Computers and Elec

journal homepage: www.ll rights reserved.sing hyperspectral reectance imaging

oad, Hangzhou 310058, China

ects on oranges, a hyperspectral imaging system has been built for acquir-range samples in the spectral region between 400 and 1000 nm. Orangesring, thrips scarring, scale infestation, canker spot, copper burn, phytotox-and normal surface were studied. Hyperspectral images of samples wereponent analysis (PCA) with the goal of selecting several wavelengths that

le at ScienceDirect

onics in Agriculture

evier .com/locate /compag

dredths of a second (Kim et al., 2002). In order to achieve high pro-cessing speeds, sometimes inspection system worked with low

nicsresolution images, or used more advanced digital signal processor.However, the low resolution images reduced the accuracy of thesystem, especially for detection of some very small defects, suchas scale infestation on citrus surface, and the advanced hardwareincreased cost of system.

Hyperspectral imaging method, which combines the features ofimaging and VIS/NIR spectroscopy to simultaneously acquire spa-tial and spectral information, has attracted the interest of research-ers as a powerful tool for detecting a variety of agriculturalproducts. Examples include detecting bruises on apples (Xinget al., 2005; ElMasry et al., 2008), bruises on pickling cucumbers(Ariana et al., 2006), pork marbling (Qiao et al., 2007), contaminantdetection on poultry carcasses and cantaloupes (Lawrence et al.,2006; Vargas et al., 2005), pits in tart cherries (Qin and Lu, 2005),nematodes in cod llets (Heia et al., 2007), cracks in shell eggs(Lawrence et al., 2008), and so on. In order to detect citrus surfacedefects, Gmez-Sanchis et al. (2004, 2008) developed multispectralimaging to detect infections caused by Penicillium digitatum in cit-rus fruits. To investigate the detection of canker on citrus fruit sur-face, Qin et al. (2008, 2009) utilized principal component analysis(PCA) together with a simple threshold classier and spectral infor-mation divergence (SID) classication method to discriminate can-ker on grapefruit from other peel diseases using hyperspectralimages with an accuracy of 92.7% and 95.2%, respectively. These re-search works provided good references and resources for dealingwith various problems associated with detection of citrus surfacedefects. However, to our knowledge no attempts were made to dif-ferentiate different defect types such as insect damage, wind scar-ring, thrips scarring, scale infestation, canker spot, copper burn,phytotoxicity, heterochromatic stripe using hyperspectral imaging.In addition, hyperspectral imaging technology has not yet been di-rectly implemented in an in-line system for automated qualitydetection because its time requirements for image acquisitionand analysis are too great (Mehl et al., 2002). Usually, the hyper-spectral data can be used to determine optimal wavelengths fordeveloping multispectral imaging system. The multispectral imag-ing approach in conjunction with selected wavelengths is favorablefor rapid in-line assessments (Kim et al., 2002, 2005; Liu et al.,2007).

The main objective of the present study was to investigate thepotential of using a hyperspectral imaging for detecting variouscommon defects on orange surfaces. For this purpose, the researchwas conducted through (1) development of a hyperspectral imag-ing system with a spectral region from 400 to 1000 nm to detectvarious common defects on orange surfaces; (2) determination ofeffective wavelengths for skin defects detection based on principalcomponent analysis method; (3) development of an algorithm toidentify stem-ends from true defects; (4) development of a simplealgorithm to isolate defected areas from sound surfaces. The ulti-mate purpose was to develop a faster andmore efcient multispec-tral method for real-time inspection of orange defects.

2. Materials and methods

2.1. Orange samplesFrom these studies, the researchers were more interested indetecting the defects based on color images and more complexalgorithms. In agricultural production systems, the time availableto evaluate individual object normally ranges from tenths to hun-

J. Li et al. / Computers and ElectroThe orange samples were hand picked from two different com-mercial orchards in Jiangxi during the harvest season of 2009 and2010, respectively. A total of 460 samples were separated intonon-defect and defect groups by visual inspection. The defects in-cluded insect damage, wind scarring, thrips scarring, scale infesta-tion, canker spot, copper burn, heterochromatic stripe andphytotoxicity. The diseases on the fruit surfaces show differentsymptoms. Wind scar, which is caused by leaves, twigs, or thornsrubbing against the fruit, is a common physical injury on the fruitpeel, and the scar tissue is generally gray (Qin et al., 2009). Scaleinfestation, insect damage, and thrips scarring are caused by insect,which generate surface blemishes during the fruit growing season.The armor of the scale is 23 mm long, dark brown in appearance.Lesions of insect damage and thrips scarring are caused by someinsect bites and the color of surface blemishes are gray and brown,respectively. Heterochromatic stripe fruit is characterized by non-uniform surface color. Those regions commonly appear as deepyellow stripe shape. Citrus canker is caused by bacteria, and it isfeatured with conspicuous dark lesions. Most circular in shape,canker lesions vary in number, and they are supercial (up to1 mm deep) on the fruit peel (Qin et al., 2009; Schubert et al.,2001). Number of the canker lesions on every orange surface in thisstudy was approximately in the range of 527. Copper burn scar, asa non-infectious disease, is caused by high temperature from longtime sun exposure. The affected areas exhibit tan lesions in appear-ance. Phytotoxicity is caused by residual pesticide and exhibitbrown around fruit calyx (Li et al., 2010). The area of infected re-gion is bigger than one-seventh of fruit surface area in this study.Fig. 1 shows samples with various common peel defects andstem-end.

Twenty samples of each type of skin defect and thirty samplesof normal oranges were selected from the 2009 data set as trainingset and used to develop the algorithm. In addition, thirty samplesof each peel type were selected from the 2010 data set as test setand used to evaluate the performance of algorithm for this study.All the samples were washed and treated with chlorine andsodium o-phenylphenate (SOPP) at Machine Vision Lab of Collegeof Biosystems Engineering and Food Science, Zhejiang University,Hangzhou. These samples were then stored in an environmentalcontrol chamber maintained at 5 C and they were removed fromcold storage about 2 h before imaging to allow them to reach roomtemperature.

2.2. Hyperspectral imaging system

A schematic diagram of developed hyperspectral imaging sys-tem with a spectral resolution of approximately 0.58 nm is shownin Fig. 2. The system consists of ve components: an imaging spec-trograph (ImSpector V10E-QE, Spectral Imaging Ltd., Oulu, Finland)coupled with a standard C-mount zoom lens (V23-f/2.4, SpecimLtd., Finland), two 150 Watt (W) halogen lamps assemblies(SCHOTT DCR III, SCHOTT North America, Inc., USA) provided a uni-form VISNIR illumination for the sample in the eld of view of theoptics, a Hamamastsu monochrome linear CCD camera (C8484-05G, Hamamatsu Photonics, Japan) with 1344 effective pixels, asample transportation plate (PSA200-11-X, Zolix Instruments Co.,Ltd., Beijing, China), and a computer (ACER, InterCoreTM24400@2.00 GHz, RAM 1.00G). The spectrograph has a prism-grat-ing-prism (PGP) element, which is a holographic transmission grat-ing. During measurements with the spectrograph-cameraassembly, the system is well shielded from the environment tominimize interference from ambient light (Wallays et al., 2009).The PGP disperses the incoming light corresponding to a pixel inthe scanned line into its spectral components and projects therange from 400 to 1000 nm onto the CCD of the camera. The sizeof the acquired images is 1344 by 700 pixels with a resolution of

in Agriculture 78 (2011) 3848 399.25 pixels/mm. The wavelength range between 550 nm and900 nm was used in this investigation due to inefciencies of thesystem at certain wavelength regions (e.g., low light output in

d sc

nicsFig. 1. Different peel types. From top to bottom and left to right: insect damage, winstripe, phytotoxicity and stem-end.

40 J. Li et al. / Computers and Electrothe VIS < 550 nm, and low quantum efciency of the CCD in theNIR > 900 nm).

2.3. Hyperspectral image acquisition

The laboratory-based system was operated in a darkenedinspection chamber where only the halogen light sources wereused. In order to acquire more accurate data, some parametersneed be adjusted before the images were acquired. In this study,the exposure time was adjusted to 200 ms and the speed of fruitmovement was adjusted to 0.12 cm/s throughout the test. The ob-ject distance was set 42.5 cm. The average illumination intensityfrom samples surface was 27333 lux acquired through a lux gauge(TES1336A). During image acquisition, the orange samples wereplaced on a tray painted with at black paint that were xed onthe positioning table (Fig. 2), manually orienting the side of thefruit that contained the defects towards the camera. The cameraand spectrograph were then used to scan the oranges line-by-lineas the transportation plate moved the oranges through the eld ofview of the optical system. The line scan data were saved and pro-cessed later to create hyperspectral image cubes containing spatialand spectral data. The spectral images were acquired based onSpectral Cube_v2_75 software (Spectral Imaging Ltd., Finland).However, after images were acquired, we found that several defec-tive areas located on the border of the fruit in acquired images dueto human fatigue error. These images were also used in our study.

Fig. 2. Schematic of hyperspectral imaging system.Due to the uneven intensity of light source in different bandsand the existence of dark current in CCD camera, some bands withless light intensity acquired the bigger noises (Polder et al., 2003).Therefore, the hyperspectral images need to be calibrated with awhite and a dark references. The dark reference was used toremove the dark current effect of the CCD detectors, which arethermally sensitive. The dark image (with 0% reectance) was col-lected by turning off all light sources and covered the lens with ablack cap. A Teon white board with the 99% reection efciency(Spectralon, Labsphere Inc.) was used to obtain white reference im-age. The corrected image (R) was calculated using Eq. (1) (Mehlet al., 2002; Xing et al., 2005; ElMasry et al., 2009):

R Ro RdRr Rd 1

where Ro is the acquired original hyperspectral image, Rr is thewhite reference image, Rd is the dark image.

2.4. Data processing and analyzing

All data processing and analyzing were performed using theEnvironment for Visualizing Images software program (ENVI 4.6,Research System Inc., Boulder, CO, USA) and Matlab 2008a (TheMathWorks Inc., Natick, USA) with the image processing toolbox.

This research uses the ENVI software package for the applica-tion of PCA to the hyperspectral images of oranges. In the process

arring, thrips scarring, scale infestation, canker spot, copper burn, heterochromatic

in Agriculture 78 (2011) 3848of creating the PCA images, a correlation matrix of the image is cal-culated. This correlation matrix is then used to compute the eigen-values. The eigenvalues are equivalent to the variance of eachprincipal component (PC) image. These PC images are ordered inthe decreasing degree of variance sizes, where rst PC accountsfor the largest variance (Liu et al., 2007). The more details on thismethod can be found in Malinowski and Howery (1980).

In this study, the PCA was used to reduce spectral dimensional-ity of the hyperspectral reectance images, and to aid in visualizingthe hyperspectral data to determine the several dominant wave-bands responsible for discriminating defects from normal orangesurfaces.

Before applying the PCA and band ratio, a binary mask was cre-ated to produce an image containing only the fruit, avoiding anyinterference from the background that could reduce discriminationefciency. Image at 750 nm was used for this task because it ap-peared the best contrast between the fruit surface and the back-ground and can be segmented easily by setting a simplethreshold value. Subsequently, individual principal component(PC) images were visually evaluated to determine PC images with(1) the least variation in normal orange surfaces and (2) the largest

contrast between defective areas and sample surfaces. Each PCimage is a linear sum of the original images at individual wave-lengths multiplied by corresponding (spectral) weighing coef-cients. Several wavelengths with high (local maxima) and low(local minimum) weighing coefcients from the PC image were se-lected as the dominant wavelengths (Vargas et al., 2005). Principalcomponents images using only the selected dominant wavelengths(multispectral images) were re-calculated. Because band ratioimages can effectively enhance the contrast between different re-gions and produce more uniform responses across the orange sur-face (Vargas et al., 2005), the two-band ratio method was also usedin this study. The two-band ratio was performed as Eq. (2):

Qt=k Rt 2

image.

J. Li et al. / Computers and Electronics3. Results and discussion

3.1. Hyperspectral reectance spectra

The representative regions of interest (ROIs) reectance spectraof orange samples studied in the wavelength range between 550and 900 nm are shown in Fig. 3. These spectra were extracted fromthe hyperspectral image data of training set and were an average oftwenty spectra (one per orange) for each type of peel condition, ex-cept for stem-end and sound orange which were used to obtain themean spectra from fteen spectra (one per orange) per type. Eachspectrum was obtained from a rectangular ROI, varying in sizefrom 80 to 100 pixels.

The reectance of spectra depicted in the VIS region was lowerthan in the NIR region over the entire spectral region. The spectraof sound peel showed higher reectance comparing to the

550 600 650 700 750 800 850 9000

10

20

30

40

50

60

70

80

Refl

ecta

nce(

%)

Wavelength(nm)

Insect damage Wind carringStem TThrips scarringScale infestation PhytotoxicityHeterochromatic stripe Canker spotCopper burn SSoundRk

where Qt/k represents a quotient of spectral reectances, and Rt andRk are reectance intensities at t nm and k nm, respectively.

Finally, the multispectral PC images and two-band ratio imageswere combined and subjected to a simple thresholding method tosegment the defective areas from the normal areas.

In addition, morphological ltering was used during defectssegmentation with an aim of removing undesired small size pixels(noises) in the binary images. One step morphological openingoperation based on a rectangle structuring element with a 3 3kernel size in MATLAB morphological lter tools were used in thisstudy and dened as erosion of the image by structuring element,followed by a dilation of the result by same structuring element.Structuring element with a 3 3 kernel size was used mainly con-sidering effectively removing the noises and retaining some smalldefects like scale infestation as many as possible in defect binaryFig. 3. Representative ROIs reectance spectra obtained from orange samples withdifferent types of peel conditions.defective peels and stem-ends in the spectral region between600 and 775 nm. Therefore, the bands from VIS region could bemore adequate for defects detection than NIR region bands. How-ever, the spectra shown in Fig. 3 do not account for the spatial vari-ations (decreases) in intensities from the center portions towardthe edges. The single band image was also attempted to segmentdifferent types of defects studied by using a simple thresholding.The results showed that it was hardly to get the target due to vari-ations in the gray level within the defective area and the surround-ing surface, which was in agreement with results achieved byBenneden and Peterson (2005).

On the one hand, it was reasonable to observe that the spectralfeatures of heterochromatic stripe fruit were similar to sound fruitsurfaces, which made the detection of this defect very difcult. Onthe other hand, it was also practically very difcult to identify het-erochromatic stripe area from sound skins based on RGB imagesdue to the color similarity (Fig. 1). In addition, one common featurealso observed from stem-end and phytotoxicity in the spectra isabsorption of chlorophyll a at approximately 689 nm. The spectraof most defective surfaces have maxima located approximately at810 and 875 nm, respectively. On the basis of these features, bandratio algorithms could be promoting to identify stem-ends fromother defects.

3.2. PCA in the VIS to NIR region of the spectrum

The rst three images (denoted by PC-1 to PC-3) obtained fromPCA for the hyperspectral reectance images of oranges samplesusing all 599 wavelengths in the region from 550 to 900 nm areshown in Fig. 4. The PCA provides a means to reduce the high spec-tral dimensionality of image data. Features such as wind scarringand heterochromatic stripe which are not readily visible in theindividual images are more apparent in these images. In the rstprincipal component (PC-1) images, intensity decreases from thecenter to the edges of oranges, and they do not provide more un-ique features than the original untransformed hyperspectralimages. The PC-2 images demonstrate more information of defectson fruit surfaces. The defective regions could be clearly identiedin the PC-2, especially for insect damage, wind scarring, thripsscarring, copper burn, phytotoxicity and heterochromatic stripe.In the PC-3 images, only the insect damage and heterochromaticstripe could be more effectively identied comparing to PC-2.Starting from PC-4 (gures not shown), the transformed imagesno longer possess meaningful information, and they are not usefulfor oranges surface defects detection. Based on visual assessment,the PC-2 images provide the better peel defects discrimination.However, the noises are also observed in some sample surfaces,such as insect damage, canker spot and stem-end. The main reasonmay be due to too many bands used to perform PCA.

3.3. PCA in the VIS region of the spectrum

The gray levels variations between defective and sound skin re-gions were mainly affected by the spectra from VIS region. Thespectra in the NIR region were usually not sensitive to the varia-tions. Thus, PCA on the full wavelength region (VIS to NIR) mayweaken the contrast of different regions on fruit surface. In general,the band region from 380 to 780 nm is known as VIS region(Hernndez-Andrs et al., 2001; Skoglund et al., 2004). In addition,the image information below 600 nm shows poor features in ourstudy. The poor features may be brought because chemical compo-nents in the orange peel are not sensitive to this waveband region.Therefore, the hyperspectral images in the wavelength range of

in Agriculture 78 (2011) 3848 41600780 nm were used to perform PCA for further analysis.Fig. 5 illustrates representative PC-1, PC-2 and PC-3 images

obtained from the PCA of the 600780 nm hyperspectral

noises were easy to be misclassied as defects (false positive). In

the defected regions of fruit surfaces appear dark in PC-2 and PC-

Fig. 4. First three principal component images obtained using the entire spectral region from 550 to 900 nm for (a) insect damage, (b) wind scarring, (c)thrips scarring,(d)scale infestation, (e)canker spot, (f) copper burn, (g) phytotoxicity, (h) heterochromatic stripe and (i) stem-end. PC-1PC-3 are the rst, second and third principalcomponents, respectively.

42 J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848addition, using too many wavelengths (309 spectral channels witha spectral resolution of approximately 0.58 nm) was also not effec-tive to develop multispectral system for defects detection.

3.4. Selection of optimal spectral wavebands

The weighing coefcients for the PC-2 obtained by using imagesacross the entire spectral region are shown in Fig. 6a for wind scar-ring, insect damage, scale infestation and thrips scarring, and 6b forcanker spot, copper burn, heterochromatic stripe, phytotoxicityand stem-end, respectively. The weighing coefcients for the PC-2 obtained by using images from VIS region are shown in Fig. 6cand d, respectively. The peaks and valleys indicated the dominantreectance image data. The PC-1 images reected a weighted sumand showed effects similar to those observed in the entire spectralregion (Fig. 4). Subsequent PC-2 images depicted the best contrastbetween skin defects and normal orange surfaces for differenttypes of samples. PC-3 images did not give any useful informationfor defects identication. Compared to PC-2 images in Fig. 4, thePC-2 images shown in Fig. 5 were more effective to identify variouspeel defects. It was also noticed that some obvious noises remainedon orange surfaces, such as normal surface with stem-end. Thesewavelengths. Therefore, six wavebands from 550 to 900 nm were

Fig. 5. First three principal component images obtained using the VIS region from 60infestation, (e) canker spot, (f) copper burn, (g) phytotoxicity, (h) heterochromatic striperespectively.3 images, and turn bright in PC-4 images. The surface defects inPC-5 images are not as evident as those in PC-2, PC-3 and PC-4images because of the relatively low contrasts between the defec-tive regions and the sound skins. The resultant multispectral PCchosen, which were centered at around 630, 691, 769, 786, 810and 875 nm, respectively. In addition, two wavelengths at around691 and 769 nm from VIS region were also singled out for furtheranalysis.

3.5. PCA on selected optimal wavebands

The principal components analysis was rst carried out on thesix optimal wavelengths (630, 691, 769, 786, 810 and 875 nm) in-stead of the full wavelength range. The rst ve PC images areshown in Fig. 7. A visual inspection of the ve PC images revealedthe major features such as defected regions and stem-ends becamemore evident in these transformed images except for PC-1 and PC-5. The useful image features were enhanced from PC-2 to PC-4.

Since each PC image shown in Fig. 7 is a linear sum of the origi-nal images at six optimal wavelengths multiplied by correspondingweighing coefcients, the intensity value of defective areas in thePC images changed with different PC images. As shown in Fig. 7,images (i.e., PC-1, PC-2 and PC-3 in Fig. 7) obtained from the

0 to 780 nm for (a) insect damage, (b) wind scarring, (c) thrips scarring, (d) scaleand (i) stem-end. PC-1PC-3 are the rst, second and third principal components,

nicsJ. Li et al. / Computers and Electromultispectral PCA also gave similar characteristics in appearanceto those (i.e., PC-1, PC-2 and PC-3 in Fig. 4) obtained on the fullwavelength region. However, the multispectral PC images weremore effective to identify defects due to decreasing noises fromtoo many bands. In addition, less spectral bands were desired todevelop robust and rapid multispectral imaging systems suitablefor skin defects detection. The resultant PC-2, PC-3 and PC-4images shown in Fig. 7 clearly demonstrated that they all may beused to segment defects. However, compared to PC-3 images, thePC-2 and PC-4 were more sensitive to illumination variations.PC-2, for example, the lighter region (center portions) of originalimages appeared lower intensities than other part after performingthe PCA. The sensibility suggested that the PC-2 and PC-4 may beinadequate for very reliable detection of various skin defects. Basedon the inspection and the comparisons above, the PC-3 imagesshowed great potential for discriminating various common orangepeel defects from sound skins. Therefore, PC-3 images were chosenfor performing the images classication.

Fig. 8 illustrates the second principal component images (PC-2)obtained by PCA of hyperspectral reectance images using two se-lected wavebands (691 and 769 nm) images in the VIS region. Be-cause the PC-1 for two wavebands (691 and 769 nm) exhibitedsimilar results in appearance to those of the corresponding PC-1images shown in Figs. 4, 5 and 7, only PC-2 images are shown inFig. 8. Based on visual assessment, the PC-2 (Fig. 8) appeared toalso provide the best orange peel defects detection regardless ofdefect types and illumination variations (Note that a dome lampcan be effective to prevent the effect from illumination variations,

Fig. 6. Weighing coefcients for the PC-2 that resulted from using the full wavelength reand (c) for wind scarring, insect damage, scale infestation and thrips scarring, and (b) anend.in Agriculture 78 (2011) 3848 43which is not the aim of this work). Therefore, PC-2 (Fig. 8) imageswere also chosen for further analysis.

3.6. Band ratio images for stem-ends identication

In the PC images (i.e., PC-2, PC-3 and PC-4 in Fig. 7 or PC-2 inFig. 8), it was very easy to observe that the characteristics ofstem-ends resembled those of the peel defects. Therefore, thestem-ends could be misclassied as defects (false positive). Basedon the spectral responses in Fig. 3, the two-band ratio using 689and 810 nm pair or 689 and 875 nm pair could be potential todetect stem-ends because of the reectance spectra of stem-endsregion exhibiting local minimum value at 689 nm, and local max-imum values at 810 and 875 nm. To develop robust and rapid mul-tispectral imaging systems suitable for skin defects detection, lessspectral bands were desired. On the one hand, the wavelength at689 nm was replaced by a wavelength at 691 nm. On the otherhand, two-band ratio images based on wavelengths at 691 and769 nm were also observed. The ratio images were obtained andshown in Fig. 9.

Compared to the resultant images, the R769/R691 and R810/R691 ratio images were ineffective in producing images for distin-guishing the stem-end from most surface defects. On the contrary,the R875/R691 ratio images showed clear stem-end region (whitearea in Fig. 9), which suggested much promise in the detection ofstem-ends because of most peel defects regions exhibiting darkerfeatures. In the study, it was found that sometimes canker spotsand phytotoxicity regions could give similar intensity value to

gion (550900 nm) and the VIS wavelength region (600780 nm), respectively: (a)d (d) for canker spot, copper burn, heterochromatic stripe, phytotoxicity and stem-

Fig. 8. The second principal component images based on the two selected wavebands (691 and 769 nm) in the VIS region for (a) insect damage, (b) wind scarring, (c) thripsscarring, (d) scale infestation, (e) canker spot, (f) copper burn, (g) phytotoxicity, (h) heterochromatic stripe and (i) stem-end.

Fig. 9. Representative two-band ratio images (R769/R691, R810/R691and R875/R691) for (a) insect damage, (b) wind scarring, (c) thrips scarring, (d) scale infestation, (e)canker spot, (f) copper burn, (g) phytotoxicity, (h) heterochromatic stripe and (i) stem-end.

Fig. 7. Principal component images based on the six selected wavebands (630, 691, 769, 786, 810 and 875 nm) for (a) insect damage, (b) wind scarring, (c) thrips scarring, (d)scale infestation, (e) canker spot, (f) copper burn, (g) phytotoxicity, (h) heterochromatic stripe and (i) stem-end. PC-1PC-5 are the rst, second, third, fourth and fthprincipal components, respectively.

44 J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848

stem-end in R875/R691 ratio images due to the two types of de-fects usually appearing slightly green skin characteristic. However,in most situations, the stem-ends were signicantly lighter thancanker spots and phytotoxicity region in the R875/R691 ratioimages due to greener color characteristic for stem-ends region.

3.7. Defects detection algorithm

Fig. 10 demonstrates major procedures for multispectral imagesprocessing and detection for thrips scarring identication using anorange sample with stem-end and thrips scarring scar on the fruit.Based on analysis in Section 3.5 and 3.6, two wavebands images at691 nm and 769 nm and two wavebands images at 691 nm and875 nm are effective for detection of defects and identication ofstem-end, respectively. Therefore, three wavebands (691, 769and 875 nm) images were used in Fig. 10. Firstly, a mask templatewas created using a single-band image at 750 nm, and three wave-bands (691, 769 and 875 nm) images were masked using the tem-plate to exclude the background that could affect the results. Then,on the one hand, the images at 691 and 875 nm were rst used toproduce ratio image (R875/R691). Afterwards, a global threshold

for stem-end binary images and single one step morphologicalopening operation for defect binary images was used to removethese noises (Fig. 10). Note that two steps dilation operation forstem-end binary image is used in order to compensate the stem-end as completely as possible in addition operation. The resultantbinary image with segmented defects is shown at the left bottomimage in Fig. 10. Two global threshold values of 0.8 and 0.23 usedin the algorithm was chosen based on spatial prole plots forstem-end and defects. Fig. 11 shows R875/R691 ratio image andcompensated PC-2 image in Fig. 10. The plots adjacent to thesetwo images illustrate spatial variations in intensity values (percentreectance) along the white lines in the images, respectively. Thedotted horizontal lines in the prole plots represent threshold linesfor stem-end and thrips scarring segmentation, respectively. Notethat although only one representative orange with stem-end anddefect are shown in Fig. 11, selection of threshold values are gen-erally based on the results observed in all training samples.

In terms of stem-ends identication, canker spots and phyto-toxicity for R875/R691 images sometimes could be misclassiedas stem-ends if only intensity information was used. However,the area of region infected by phytotoxicity is usually signicantly

J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848 45value of 0.8 was applied to the ratio image to separate stem-endfrom the fruit surface. On the other hand, the PCA based on twoimages at 691 and 769 nm was also performed to generate twoPC images (PC-1 and PC-2). Subsequently, the addition operationwas performed between stem-end binary image and PC-2 imageto increase stem-end gray value (stem-end gray compensation).Finally, the compensated PC-2 image was subjected to a simplethresholding method with a global threshold value of 0.23 to sep-arate the defective skins from the normal fruit surfaces. Note thatow charts of the key steps for defects detection algorithm basedon PCA from six (630, 691, 769, 786, 810 and 875 nm) and two(691 and 769 nm) optimal wavelengths are similar. The only differ-ence is if the PC images are generated by six wavebands, the addi-tion operation was performed using stem-end binary image andresultant PC-3 image.

In some cases, there are some very minor defects/blemishes(noises) on normal orange surfaces. Because these noises may carrythe same spectral signature as defects detected in this work, somenormal orange could be misidentied as defected oranges. In orderto overcome this problem, one step morphological openingoperation (Erosion and Dilation) and two steps dilation operationFig. 10. Flow chart of the key steps involved inbigger than stem-end area and the number of canker spots on theorange surface is more than one. Thus, the size and number ofmarked region for stem-end binary image, as two characteristics,can be used to further identify stem-end. Firstly, the stem-endbinary image (Fig. 10) was obtained and marked. If only one re-gion in the stem-end binary image was marked and its size wasless than 278 pixels (Note that a stem-end region vary in sizefrom 164 to 275 pixels in this research), this region was classiedas stem-end. Then, the addition operation for stem-end gray va-lue compensation was performed before defects segmentation.Otherwise, any other case, the addition operation was not per-formed and corresponding PC images (PC-3 for six bands andPC-2 for two bands) directly were used to segment defects. Bydoing this, although there was a very low percentage of falsedetections for samples with canker spots and phytotoxicity (Notethat the false detections are acceptable due to only occurring indefective fruit), the classication procedure above-mentionedcan be simplied.

An example of analysis performed by the proposed approach foreach peel type is shown in Fig. 12. The illustrated nine sampleswere randomly selected from test set. In order to obtain moreorange peel defects detection algorithm.

Fig. 12. Example of defects detection for (a) insect damage, (b) wind scarring, (c) thrips scarring, (d) scale infestation, (e) canker spot, (f) copper burn, (g) phytotoxicity, (h)heterochromatic stripe and (i) stem-end.

Fig. 11. The two-band ratio image (R875/R691) (a) and compensated PC-2 image (b) in Fig. 10. The images are accompanied by spatial prole plots for the white horizontallines on the orange, respectively. Note that the white lines transect the stem-end and thrips scarring on the orange, respectively.

46 J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848

distinct contrast, RGB images of fruit were showed in the rst row,and the defective regions were marked manually using a graphicaltool. Second row shows the two-band ratio images (R875/R691).Third and fourth rows correspond to PC-2 images obtained usingthe two selected wavebands (691 and 769 nm) in the VIS regionand PC-3 images obtained using six selected wavebands (630,691, 769, 786, 810 and 875 nm) in the full wavelength region,respectively. Fifth row corresponds to binary images obtainedusing developed algorithm based on two-band ratio (R875/R691)images and PC-2 images. Sixth row corresponds to binary imagesobtained using developed algorithm based on two-band ratio(R875/R691) images and PC-3 images. It can be noticed that defectspositions in the RGB images are different with those in the PC andratio images because the fruit was imaged using the different cam-eras (hyperspectral imaging cameras and RGB cameras) in differentinspection chambers, so moved from one image to other but pre-senting the same defects images for each type. In the binaryimages, the normal fruit surfaces and stem-end were converted

two Thrips scarring samples, two Canker spot samples, one Phy-totoxicity sample, and seven Heterochromatic stripe samples.The reectance properties for heterochromatic stripe was close tothose of normal skin (see Fig. 3), especially for the lightly hetero-chromatic stripe. That probably is the reason for the lowest identi-cation rate for this case. However, if identication accuracy is notrequired to be very high, fruit with heterochromatic stripe is usu-ally considered as sound fruit. For undetected wind scarring, can-ker spot, one insect damage, and phytotoxicity samples, thelightly defects presented on orange surfaces. After PCA was per-formed, the contrast between defective areas and sound skins islower. In this study, we also attempted to increase threshold valueT2 from 0.23 to 0.39 in order to improve detection rate of defectiveoranges. However, although the defects detection rate was im-proved from 92.9% to 95.4%, it was also found in this investigationthat false positive rate was increased from 0% to 3.3% along withincreasing the threshold value, showing the selection of thresholdvalues is very important in defects detection. The cases of incorrect

nt d

ed

75 nand

J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848 47to zero, and the remaining white regions represented defects iso-lated from the normal fruit surfaces, showing the effectiveness ofthe proposed classication algorithm for defects detection.

3.8. Identication results

The algorithms for multispectral image processing and classi-cation described above were evaluated using 270 independentsamples. The test results for different types of defects are shownin Table 1. A total of 270 fruit samples were divided into two clas-ses: Defected class including 240 samples with eight types of skindefects, 30 samples for each type, and Normal class including 15samples with stem-end and 15 samples without stem-end. Allsamples were evaluated using two detection methods: one wastwo-band ratio (R875/R691) and PCA using two wavelengths(691 and 769 nm), the other was two-band ratio (R875/R691)and PCA using six wavelengths (630, 691, 769, 786, 810 and875 nm). As shown in Table 1, the overall detection accuracy forthe tested samples was 93.7% and 91.5% with no false positives(0 out of 30 normal oranges) based on two methods, respectively.

In terms of the rst detection method, all Scale infestation,Copper burn and Normal samples were correctly identied underT1 = 0.8, T2 = 0.23. Note that the T1 and T2 represent the thresholdvalues applied to two-band ratio images and PC images (seeFig. 11), respectively. Seventeen samples were undetected, includ-ing two Insect damage samples, three Wind scarring samples,

Table 1Test results for developed algorithms based on 270 independent samples with differe

Class Peel types Number Misclassi

BRa+PCA(6 bandsb)

Defected (n = 240) Insect damage 30 2(2)Wind scarring 30 3(1)Thrips scarring 30 2(2)Scale infestation 30 0(0)Canker spot 30 3(2)Copper burn 30 0(0)Phytotoxicity 30 2(0)Heterochromatic stripe 30 11(9)

Normal (n = 30) Normal with stem 15 0(1)Normal without stem 15 0(0)

Total 9 270 23(17)

a The BR means the band ratio algorithm.b The 6 bands and 3 bands mean six wavelengths (685, 710, 769, 786, 810 and 8c The accuracies without brackets were obtained under T1 = 0.8, T2 = 0.23. The T1respectively.d The accuracies inside the brackets were obtained under T1 = 0.8, T2 = 0.39. The T1 and

respectively.detection of thrips scarring and other insect damage are different:what happened here is that these damages located on the border ofthe fruit in the detected image, indicating that the identicationalgorithm could be affected by the position of defected regionson fruit surface in the detected image. Therefore, the accuracy forthe identication using the simple global thresholding methodmay be decreased for detecting the orange samples when defectslocated on the border of the fruit. In general, two strategies couldbe potential to resolve this problem. One method proposed inmany literatures was performed by simply not inspecting the bor-ders of the fruit in the images. Not inspecting the borders, however,does not imply losing efcacy, since in the in-line automatic com-puter vision systems for fruit inspection, the fruits rotate, thusallowing the system to inspect most of the fruit surfaces by acquir-ing different images while they pass below the camera (Blascoet al., 2007a). The other method was that local threshold valuewas applied instead of global threshold method. In order to use thisprinciple, the fruit detected was rst segmented into two parts,border region (circular region) and remaining region (middle re-gion). Then, different threshold values were applied to two regions.

4. Conclusions

In this investigation, hyperspectral reectance images wereevaluated for detecting various common defects on the orangesurface in the wavelength range between 550 and 900 nm. This

efects types and normal surfaces.

Accuracy (%)

BR + PCA BR + PCA BR + PCA(3 bandsb) (6 bands) (3 bands)

2(2) 93.3c (93.3)d 93.3 (93.3)3(1) 90.0 (96.7) 90.0 (96.7)2(2) 93.3 (93.3) 93.3 (93.3)0(0) 100.0 (100.0) 90.4 100.0 (100.0) 92.92(0) 90.0 (93.3) (93.3) 93.3 (100.0) (95.4)0(0) 100.0 (100.0) 100.0 (100.0)1(0) 93.3 (100.0) 96.7 (100.0)7(6) 63.3 (70.0) 76.7 (80.0)0(1) 100.0 (93.3) 100 100.0 (93.3) 1000(0) 100.0 (100.0) (96.7) 100.0 (100.0) (96.7)

17(12) 91.5 (93.7) 93.7 (95.6)

m) and the three wavelengths (691, 769 and 875 nm), respectively.T2 represent the threshold values applied to two-band ratio images and PC images,T2 represent the threshold values applied to two-band ratio images and PC images,

developing an efcient multispectral imaging system, including

Acknowledgements

48 J. Li et al. / Computers and Electronics in Agriculture 78 (2011) 3848The authors gratefully acknowledge the nancial support pro-vided by National Natural Science Foundation of China (No.30825027).

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Detection of common defects on oranges using hyperspectral reflectance imaging1 Introduction2 Materials and methods2.1 Orange samples2.2 Hyperspectral imaging system2.3 Hyperspectral image acquisition2.4 Data processing and analyzing

3 Results and discussion3.1 Hyperspectral reflectance spectra3.2 PCA in the VIS to NIR region of the spectrum3.3 PCA in the VIS region of the spectrum3.4 Selection of optimal spectral wavebands3.5 PCA on selected optimal wavebands3.6 Band ratio images for stem-ends identification3.7 Defects detection algorithm3.8 Identification results

4 ConclusionsAcknowledgementsReferences