Research ArticleOnline Measuring and Size Sorting for Perillae Based onMachine Vision

Bo Zhao,1 Ye Wang,1 Jun Fu ,1,2 Rongqiang Zhao ,2 Yashuo Li,1 Xin Dong,1 Chengxu Lv,1

and Hanlu Jiang1

1Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China2College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

Correspondence should be addressed to Jun Fu; fu_jun@jlu.edu.cn

Received 18 September 2019; Accepted 31 October 2019; Published 13 January 2020

Guest Editor: Yuan Li

Copyright © 2020 Bo Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity ofa perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend onmanual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop anapproach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and thecorresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuringand size sorting method is proposed, including image binarization, key point determination, information matching, andparameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the averagemeasuring error, the variance of measuring error, and the overall sorting accuracy are 204.175ms, 1.48mm, 0.07mm, and 93%,respectively, implying the feasibility and satisfied accuracy of the proposed approach.

1. Introduction

Perillae is widely distributed throughout Asia, and it hasattracted an increasing interest in the field of medicine andpharmacy. Perillae has been applied for bacteriostatic, detox-ifying, antitussive, and phlegm for thousands of years in tra-ditional Chinese medicine. Recently, more attentions havebeen paid to the study with respect to perillae, including theperillae herba ethanolic [1], the luteolin [2], and the rosmari-nic acid [3]. Besides, perillae is also a popular food in coun-tries such as China, Japan, and Korea. The size of perillaeindicates its quality and maturity that closely relates to itsmedical and edible value. Hence, it is of great significanceto measure and sort the size of perillae in advance. At present,measuring and sorting the size of perillae mainly depend onmanual work, which is of low efficiency and unsatisfied accu-racy. Therefore, it is valuable to develop an approach foraccurate online measurement and sort of perillae.

Machine vision (MV) is the technique to provideimaging-based automatic inspection [4–6], and it has beenwidely used in the fields of industry, such as object detectionand robot guidance [7, 8]. For the applications in agriculture,previous work has explored into various crops [9, 10]. In[11], a compact machine vision system based on hyperspec-tral imaging and machine learning is presented to detect afla-toxin in chili pepper. In [12], a hierarchical grading methodis applied for real-time defect detection and size sorting ofpotatoes. In [13], a study is conducted to predict ripeningquality in mangoes using RGB images, for which the hierar-chical clustering method is employed to classify the ripeningperiod into five stages based on quality parameters. In [14],the authors combine the MV and the support vector machineto develop an intelligent system for sorting of peeled pista-chio kernels and shells. In [15], Sabliov et al. develop anMV-based method to measure volume and surface area ofellipsoidal agricultural products by regarding the objects as

HindawiJournal of SensorsVolume 2020, Article ID 3125708, 8 pageshttps://doi.org/10.1155/2020/3125708

the sum of superimposed elementary frustums of right circu-lar cones. In [16], Yao et al. develop real-time detectioninstrumentation for aflatoxin-contaminated corn using anarrow-band fluorescence index. In [17], Pedreschi et al.present an inexpensive computer vision system for measur-ing the color of a highly heterogeneous food material suchas potato chips. In [18], Huang et al. propose an approachfor identification of defect pleurotus geesteranus based oncomputer vision. In [19], Sun et al. develop a machine visionsystem and the dynamic weighing system for the measure-ment of egg external physical characteristic and weight, suchthat the nondestructive method for online estimation of eggfreshness is achieved. Additionally, a set of studies have beenconducted on measurement, cabbage [20], flower mushroom[21], cherry [22], litchi [23], and other agricultural products[24–29].

However, the automatic online detection of perillae hasnot been explored till now. The geometry model and the cor-responding mathematical model of perillae used for auto-matic detection have not been built yet. The size sorting ofperillae still requires manual work. To address this issue, inthis study, we develop a novel approach for online measuringand size sorting of perillaes. A charge-coupled device (CCD)camera is employed to acquire the images of perillaes in thelighting box, and the MV method is proposed for image pro-cessing. The contributions of this study can be summarizedas follows.

(1) We build the general geometry model of perillae andthe corresponding mathematical model for imageprocessing

(2) We propose the MV-based method for accurateonline measuring and size sorting of perillae underthe proposed models

(3) We develop the practical system for our theoreticalmethod, the feasibility and accuracy of which are ver-ified by the experimental results

The remainder of this paper is organized as follows.Section 2 introduces the proposed geometry model andmathematical model. Section 3 provides the MV-basedonline measuring method. Section 4 presents the experimen-tal results and analysis. Section 5 concludes this paper.

2. Geometry and Mathematical Models

2.1. Geometry Model. Figure 1 shows a typical perillae formedical and food usage. The notation Lu is used to denotethe length of a perillae, i.e., the maximum size of perillae leaf.We mainly consider the length between PA and PB as it is theuseful part of perillae, regardless of the medical or food usage.The length between PB and PC , i.e., the length of perillae, isnot taken into the measurement.

Figure 2 presents the sorting principle for the size of peri-llae. In this study, the sizes are divided into four gradesaccording to Lu. As shown in Figure 2, the first grade isexpressed as “Spec M” with the size of 7 cm ≤ Lu < 8:5 cm,denoting the smallest perillae. The second grade, “Spec

L(s),” and the third grade, “Spec L(b),” are determined by8:5 cm ≤ Lu < 9:5 cm and 9:5 cm ≤ Lu < 10:5 cm, respectively.The fourth grade, expressed as “Spec 2M,” is used to repre-sent the largest size, given by 10:5 cm ≤ Lu < 12 cm.

2.2. Mathematical Model. Next, we provide the mathematicalmodel for imaging of perillae. The camera used in this studywas calibrated before it is employed for imaging. As shown inFigure 3, we captured 20 chessboard images with differentposes and then utilized the calibration tool in the OpenCVto calibrate the camera.

Let u, v, and w denote the horizontal coordinate, the ver-tical coordinate, and the position perpendicular to the chess-board in the camera coordinate, respectively, and let x, y, andz denote the corresponding coordinates in the world coordi-nate. The calibration results demonstrate the distortion error,and the reprojection error can be ignored. AssumeMðx, y, zÞis a pixel in the world coordinate system, and this pixel isthen projected into the camera coordinate system, expressed

PA

PB

Lu

PC

Figure 1: Geometry model of perillae where PA, PB, and PCrepresent the apex of perillae, the separation point of leaf andpetiole, and the endpoint of petiole, respectively.

M L(s) L(b) 2L

Figure 2: Classification of perillae sizes.

Figure 3: Classification of perillae sizes.

2 Journal of Sensors

as mðu, vÞ. Let G be the intrinsic matrix of the used camera.Based on the used calibration tool, the transformationbetween the world coordinate system and the camera coordi-nate system can be expressed as

u

v

w

2664

3775 =G

x

y

z

2664

3775: ð1Þ

The matrixG is obtained through the calibration, and theresult is

G =

1αx

0 1βx

0 1αy

1βy

0 0 1

26666664

37777775=

11345:538 0 1

606:253

0 11345:538

1470:283

0 0 1

266664

377775:

ð2Þ

By combining the geometrical model and (2), we canrewrite the size of perillae as follows:

Lu =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffipΑ,x − pΒ,x� �2 + pΑ,y − pΒ,y

� �2r

=

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqA,x − qB,x

αx

� �+

qA,y − qB,yαy

!2vuut ,

ð3Þ

where pA,x, pA,y , pB,x , and pB,y are the coordinates of points PA

and PB in the world coordinate system and qA,x, qA,y, qB,x, andqB,y are the coordinates of points PA and PB in the imagecoordinate system. Obviously, the required size can be calcu-lated out directly if parameter z and the coordinates of PAand PB are known. Hence, we introduce the proposedmethod to determine this information in the next section.

3. Method for Measuring Perillae

In this section, we provide the proposed method for measur-ing and size sorting of perillae based on the MV and the pro-posed models. The method is composed of the followingsteps, binarizing the original image, determining the posi-tions of PA and PB, matching information, and estimatingparameter z.

3.1. Image Binarization. Figure 4(a) presents the originalimage of perillaes. The image should be binarized before itis used for measuring. The binarization principle is given by

j x, yð Þ =255, 380 ≤ y ≤ 690, hb < 180, hr < 220,0, otherwise,

(ð4Þ

where jðx, yÞ is the value of the pixel ðx, yÞ in the binarizedimage. The depth of the image is 8 bits, and therefore, thevalue of white pixels is set to 255. As indicated in (4), a pixelis determined to be white as long as 380 ≤ y ≤ 690, hb < 180,and hr < 220, where hb and hr are the values of the pixel inthe blue channel and the red channel, respectively. The firstcondition, 380 ≤ y ≤ 690, ensures that the background is setto be black, and only the area containing perillaes is consid-ered. The second and the third conditions are utilized todetermine whether a pixel belongs to the area of perillae.The binarized image is shown as Figure 4(b).

3.2. Search for PA.We first derive the contour from the binar-ized image. The strategy is to set the thresholds for the largestand the smallest areas of the perillaes. Thus, the areas thatsatisfy the constraint of thresholds are selected, and theircontours are regarded as the contours of perillaes. This pro-cess can be expressed as

cjri =1, 6000 < Ai < 30000,0, otherwise,1 ≤ i ≤N

8><>:

ð5Þ

where N denotes the total number of contours in the bina-rized image, Ai denotes the area of the i

th contours, and cjri

(a) (b)

Figure 4: Image binarization.

3Journal of Sensors

is the judging result. Then, the minimum enclosing circle andthe convex hull of each perillae can be obtained. These peri-llaes are labelled as 1, 2, 3, and 4 from left to right based onthe following judgement:

pjr j =

1, 1 ≤ cj,x ≤ 320,2, 321 ≤ cj,x ≤ 640,3, 641 ≤ cj,x ≤ 960,4, 961 ≤ cj,x ≤ 1280,1 ≤ j ≤Num1,

8>>>>>>>><>>>>>>>>:

ð6Þ

where pjrj is the judgement for each position of perillae, cj,xdenotes the horizontal coordinate of the center of the jth

enclosing circle, and “Num1” denotes the amount of perillaein the captured image. The results are displayed inFigure 5(b) and are saved as “Pos1.”

Finally, we utilize the following strategy to find the end-points of the lth contours, written as p11,l and p12,l:

p11,l = arg mincpl,k

absffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffifficpl,k,x − ccl,x� �2 + cpl,k,y − ccl,y

� �2r− rl

!,

1 ≤ l ≤Num1, 1 ≤ k ≤ml ,ð7Þ

p12,l = arg mincpl,k

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffifficpl,k,x − p11,l,x� �2 + cpl,k,y − p11,l,y

� �2r,

1 ≤ l ≤Num1, 1 ≤ k ≤ml − 1,ð8Þ

where cpl,k denotes the kth pixel in the lth convex hull, cpl,k,x

and cpl,k,y are the coordinates of cpl,k, ccl,x and ccl,y are the

coordinates of the center of the lth enclosing circle, rl repre-sents the radius of the lth enclosing circle, and ml is theamount of pixels of the lth convex hull. In fact, p11,l can beregarded as the point furthest away from the center of theenclosing circle, and p12,l is the point furthest away fromp11,l. These two points are generally PA and PC . The data of“Num1,” “Pos1,” p11, and p12 are saved as “Info1.”

3.3. Search for PB. To acquire the position of PB, we conductone close operation, four erode operations, and four dilateoperations in the OpenCV for the binarized image, succes-sively. The results are presented in Figure 6(a). Then, wederive the treated contours using the strategy given by (7)and (8). The results are shown in Figure 6(b) and are savedas “Pos2.” Finally, the minimum enclosing circle and the con-vex hull of the derived contours are obtained, shown asFigure 6(c). Similar to the procedure to search for PA andPC , two endpoints, p21 and p22, are selected as the alternative

(a) (b) (c)

Figure 6: Determination of PB.

(a) (b)

Figure 5: Determination of PA.

4 Journal of Sensors

points of PB. The other one is defined as PD. The data of“Pos2,” p21, and p22 are saved into “Info2.”

3.4. Information Matching.Next, we should recognize PA andPB from p11, p12, p21, and p22. For this purpose, we first com-pute the Euclidean distance from p21 to p11, p21 to p12, p22 top11, and p22 to p12, respectively. Then, we compare the fourdistances and find out the two points that lead to the largestdistance. Assuming the two points are p12 and p22, we candraw the conclusion that PC is p12, as p12 is the alternativepoint of PA and PC , and the distance from PC to PD is alwayslarger than that of PA to PB. Repeat the procedure for all“Num1” contours of perillae, such that the position of PAand PB of each perillae can be obtained. We summarize allfour possibilities in Table 1.

3.5. Scale Factor Estimation and Size Computation. As indi-cated in (8), parameter z is the factor controlling the scaleof imaging. Hence, we utilize the following scale method toestimate z. As shown in Figure 7, the original plane is z = c2. We use A2B2 to represent the manual measurement anduse A0B0 to represent the corresponding measurement inthe camera coordinate system. Then, we manually set z toc1 to acquire the measurement A1B1 by using (8). Based onthese measurements, we have

A0B0A1B1

= fc1, ð9Þ

A0B0A2B2

= fc2: ð10Þ

Therefore, z can be calculated by

z = c2 =A2B2A1B1

: ð11Þ

To eliminate the measurement error, we repeat the aboveprocess for 20 times, and the average value is used for theexperiments, computed by

z = c2 =∑20

i=1c2i20 , ð12Þ

where c2i denotes the calculated value in the ith trial.The proposed approach is summarized in Figure 8.

4. Experimental Results

Our experimental system is shown in Figure 9, composed of alighting box and a personal computer (PC). The lighting boxcontains four light-emitting diode (LED) light located as aring on the top of the box and a CCD camera in the centerof the box. The experimental setup is described as follows.Before conducting the experiments, we manually set c1 = 50cm and obtain z = 68:765 cm by using the strategy intro-duced in Subsection 3.5. This value of z is used for all exper-iments in this study. First, we randomly chose 100 perillaes

and manually measured their sizes. Then, we used the devel-oped system to measure these perillaes.

The results by both manual measurement and automaticonline measurement are presented in Figure 10. We com-puted the measurement error for each perillae, and theresults are provided in Figure 11. Obviously, the resultsobtained by online measurement are close to those obtainedby manual measurement. As provided in Table 2, amongthe 100 perillaes, the maximum measuring error (MAME)is 3.66mm, while the minimum measuring error (MIME) is0.02mm. Most of the errors are lower than 3mm. The overallaverage measuring error (OAME) and the variance of mea-suring error (VME) are 1.47mm and 0.07mm, respectively.

5. Conclusions

We developed anMV-based approach for automatic measur-ing and size sorting of perillae. We first built the geometricmodel for perillae and the mathematical model for imaging.Based on the models, the measuring and size sorting methodwas proposed, including image binarization, key point deter-mination, information matching, and parameter estimation.We employed the CCD camera for imaging and the OpenCVtools for image processing. Experimental results have verifiedthe feasibility of our system and its high accuracy of measur-ing and size sorting. By using 100 perillaes for experiments,theMAME and theMIME are 3.66mm and 0.02mm, respec-tively. Most of the errors are lower than 3mm. The OAME

o

o0

x

y

X

Y

z = c1

z = c2

A0 B0

z = f

A1

A2

B1

B2

o1

o2

z

Figure 7: Pinhole imaging model.

Table 1: Information matching, PA and PB to p11, p12, p21, and p22.

Points leading to the largest distance PA PB

p11 and p21 p12 p22p11 and p22 p12 p21p12 and p21 p11 p22p12 and p22 p11 p21

5Journal of Sensors

and the VME are 1.47mm and 0.07mm, respectively. Fur-ther study could address on the following issues. First, mini-aturization for the developed system requires future work to

make the proposed approach more particle. Second, utilizingthe RGB image or hyperspectral image to detect the maturityof perillaes is also valuable for further study.

Image acquisition

Apply close, erode, and dilateoperations to binarized image

Num1, Pos1

Num2, Pos2

p11, p12

p21, p22

pA, pB

z

lu

Apply close, erode, and dilateoperations to binarized image

Obtain the minimum enclosing circlesand convex hullls of original contours

Obtain the minimum enclosing circlesand convex hullls of treated contours

Find the alternative points of PA

Find the alternative points of PB

Calculate the Euclideandistances from alternative points

Determine PA and PB

from alternative points

Estimate the scale factor

Compute and sort the acturalsize of perillaes

Image binarization

Search for PA

Search for PB

Information matching

Scale factor estimationand size computation

Figure 8: Outline of the proposed approach.

Lighting box LED

PC

CCD camera

Figure 9: Experimental system.

14.013.513.012.512.011.511.010.510.0

9.59.08.58.07.57.0

0 10 20 30 40 50Serial number

Manual measuring valueSystem measuring value

Leng

th (c

m)

60 70 80 90 100

Figure 10: Measuring result.

6 Journal of Sensors

Data Availability

The data used to support the findings of this study are avail-able from the corresponding author upon request.

Conflicts of Interest

The authors claim no conflicts of interest.

Acknowledgments

This work was supported by the National Key Research andDevelopment Project under Grant 2017YFD0401305.

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4

3

2

1

8 9 10 11 12 13 14

–1

Rela

tive e

rror

(mm

)

Manual measuring value (cm)

–2

–3

–4

0

Figure 11: Measuring error for each perillae.

Table 2: Overall measuring and sorting results.

ATCCI(ms)

MAME(mm)

MIME(mm)

OAME(mm)

VME(mm)

OSA(%)

Results 204.175 3.66 0.02 1.47 0.07 93

7Journal of Sensors

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