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RESEARCH ARTICLE
Grading detection of “Red Fuji” apple in
Luochuan based on machine vision and near-
infrared spectroscopy
Jin WangID, Yujia Huo, Yutong Wang, Haoyu Zhao, Kai Li, Li LiuID*, Yinggang ShiID
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100,
China
*liuli_ren_79@nwafu.edu.cn
Abstract
A quality detection system for the “Red Fuji” apple in Luochuan was designed for automatic
grading. According to the Chinese national standard, the grading principles of apple appear-
ance quality and Brix detection were determined. Based on machine vision and image pro-
cessing, the classifier models of apple defect, contour, and size were constructed. And
then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and
the cross-sectional diameter W
p
in the image of the apple. Spectral information of apples in
the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correc-
tion (MSC) and standard normal variable (SNV) transformation methods were used to pre-
process spectral reflectance data. The competitive adaptive reweighted sampling (CARS)
algorithm and the successive projections algorithm (SPA) were used to extract characteris-
tic wavelength points containing Brix information, and the CARS-PLS (partial least squares)
algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and
Brix were combined as grading indicators. The apple quality online grading detection plat-
form was built, and apple’s comprehensive grading detection algorithm and upper computer
software were designed. The experiments showed that the average accuracy of apple
defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively,
and the correlation coefficient R
p
of the Brix prediction set was 0.9469. The total accuracy of
apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection sys-
tem designed in this paper is feasible to classify “Red Fuji” apple in Luochuan.
1 Introduction
Grading for sales is strongly needed to commercialize agricultural products and boost eco-
nomic benefits [1]. Automated apple grading can reduce manual work intensity and improve
the repeatability and accuracy of results. The external quality parameters for assessing apple
grades include surface defects, color, texture, size, and shape. The internal quality parameters
for assessing apple grades include degrees Brix, acidity, vitamins, water content, soluble solids,
internal quality defects, etc [2,3]. Based on these indicators, many experts and scholars have
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OPEN ACCESS
Citation: Wang J, Huo Y, Wang Y, Zhao H, Li K, Liu
L, et al. (2022) Grading detection of “Red Fuji”
apple in Luochuan based on machine vision and
near-infrared spectroscopy. PLoS ONE 17(8):
e0271352. https://doi.org/10.1371/journal.
pone.0271352
Editor: Felix Yao Huemabu Kutsanedzie, Accra
Technical University, GHANA
Received: February 8, 2022
Accepted: June 28, 2022
Published: August 4, 2022
Copyright: ©2022 Wang et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: Shaanxi Province Key Research and
Development Projects of China (2020NY-144,
2019NY-171, 2019ZDLNY02-04), Innovative
Training Program for College Students of
Northwest A&F University (202110712136),
National Natural Science Foundation of China
(31971805), And National Key Research and
carried out relevant research on automatic apple grading. Among them, there are a relatively
large number of studies on automatic apple grading algorithms based on machine vision [4–
6]. Additionally, in terms of injury-free detection technology for agricultural products, spec-
troscopy technology has undergone rapid development [7–9]. Zhao Miao et al. developed a
robotic system for the automatic detection and classification of internal quality attributes of
apples using near-infrared spectroscopy [10]; Liu Penghui et al. used machine vision and spec-
troscopy for the non-destructive detection of apple crispness with accurate and reliable results
[11]. Tan Wenyi et al. used hyperspectral imaging to propose an accurate algorithm for apple
abrasion identification, which provided a new method for non-destructive detection [12]. Ker-
esztes et al. used shortwave infrared hyperspectral imaging combined with calibration and
glare correction techniques, on a real-time pixel basis for contusion detection of early apples
[13].
In 2020, China produced 44.066 million tons of apples, exceeding the global average pro-
duction by more than 50%, among which, approximately 1812106.13 acres of apples were
planted in Shaanxi Province, producing 11.8521 million tons [14], showing great demand for
automated apple grading machines. To carry out automatic grading of apples comprehensively
and accurately, this study designed a set of grading detection algorithms for Red Fuji apple in
Luochuan, mainly including four single quality detection algorithms and a comprehensive
grading algorithm. Firstly, three classifier models of defect, contour, and size were designed by
using machine vision, and the defect, contour, and size of the apple’s appearance were detected
successively. Secondly, near-infrared spectroscopy was used to collect the spectral information
of apples. And CARS-PLS algorithm was adopted to predict the sugar content of apples.
Finally, according to the National Standard of the People’s Republic of China for grading fresh
apples, combined with the defect, contour, size, and Brix of the detected apple, the comprehen-
sive grading algorithm of Red Fuji apples in Luochuan was designed. With the algorithm,
upper computer software was designed to display the grading detection results in real-time.
An online grading detection platform for apple quality was built to verify the effectiveness and
accuracy of the grading algorithm. Experimental results show that the accuracy of the designed
algorithm is 96.67%, which met the requirements of automatic grading of the Red Fuji apple in
Luochuan.
2 Experimental materials
The apples selected in this study are Red Fuji varieties in Luochuan, Shaanxi Province, which
were purchased from Shenggu Industrial Co Ltd in Yan’an, Shaanxi Province. Before experi-
ments, each sample was numbered, and the serial number label was attached to the bottom of
the sample. Their appearance is shown in Fig 1.
3 Principle of apple quality detection classifier
3.1 Principle of apple appearance quality grading detection
According to the National Standard of the People’s Republic of China for grading fresh apples
[15], apples are classified according to their size characteristics based on their cross-sectional
diameters, i.e., the diameter at the largest point in the cross-section, as follows: the apple with a
cross-sectional diameter >7 cm is graded large (L), the apple with a cross-sectional diameter
>6.5 cm and <7 cm is graded medium (M), and the apple with a cross-sectional diameter
<6.5 cm is graded small (S).
Similarly, the national standard for apple grading is based on varietal characteristics, shape
characteristics (fruit shape index), color, red coloring rate, pests, or other damages, and the
test indicators are as follows:
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Development Program of China
(2019YFD1002401).
Competing interests: The authors have declared
that no competing interests exist.
Excellent fruit: uniform in appearance, round and free of ribs, square, round or nearly
round, without skew, with a fruit shape index of 0.85 or more; evenly distributed color, high
brightness; smooth and delicate, evenly distributed texture, not rough, without damages,
cracks or scars.
First-class fruit: undifferentiated in appearance, nearly round or oblate, slightly deformed
but not more than 30% of the total, with a fruit shape index of 0.8 to 0.85; ripe or slightly
underripe, evenly distributed color, evenly distributed texture, not rough; without damages,
cracks or scars.
Fig 1. Red Fuji apple appearance.
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Second-class fruit: basic fruit shape, subrounded, slightly deformed, with defects in fruit
shape but still having basic features, no malformation, with a fruit shape index of 0.75 to 0.8;
slightly under- or overripe, slightly rough; slightly damaged, no cracks, slight scars on the skin,
but unaffected fruit merchantability.
Substandard fruit: variable appearance in shapes, oval-shaped fruit with large deformation,
with a fruit shape index of 0.75 or less; less evenly distributed texture, slightly under- or over-
ripe, slightly rough; with damage cracks and obvious scars on the skin.
Most of the factors affecting apple grading are sensory factors. According to the National
Standard for grading fresh apples, the defect, contour, and size of the Red Fuji apple in Luo-
chuan were selected as external quality detection indexes in this study. In general, apple surface
defects are usually judged by the human eye. Contour is generally evaluated by fruit shape
coefficient, which is usually the ratio of transverse diameter to longitudinal diameter. The
closer the fruit shape coefficient is to 1, the rounder the apple appearance. And the apple size is
generally evaluated by measuring the maximum cross diameter with a vernier caliper. The
above measurement methods of apple defect, contour, and size are physical detection methods,
which need to be completed manually. They are not efficient and automatic enough to meet
the requirements of real-time apple grading detection. Therefore, this study proposed methods
of processing apple images by machine vision to complete the real-time detection of the apple
defect area, aspect ratio, and maximum transverse diameter. Then, each external quality was
graded according to the national standards.
3.2 Principle and method of apple Brix detection
Currently, China’s national standard for apple internal quality only gives a reference index of
7% or more for fruit hardness and 13% or more for soluble solids in mature Fuji apples. The
unclear national standard for the internal quality of apples has resulted in a lack of scientific,
systematic, and operable indicators of grading evaluation based on this standard.
Customers pay attention to taste when buying apples. The most important factor affecting
the taste of apples is their sugar-acid ratio, of which the apple Brix value plays a leading role.
Current chemical methods for measuring the apple Brix value include 3, 5-dinitrosalicylic acid
colorimetric method, anthrone colorimetric method, Fehling reagent thermal titration, and so
on [16]. All the above determination methods need to use acid to hydrolyze disaccharides and
polysaccharides into reductive monosaccharides and use the reductive sugar determination
method to measure the sugar content of apples. In addition, gas chromatography and high-
performance liquid chromatography can not only measure sugar content but also determine
the composition of sugar. Generally, recovery and precision experiments are used to evaluate
the above chemical methods, and the accuracy of measured sugar content is evaluated by cal-
culating the total sugar recovery, standard deviation, and coefficient of variation. The more the
total sugar recovery is close to 100%, the smaller the standard deviation and coefficient of vari-
ation are, and the more accurate and reliable the measured sugar content is. These methods,
however, generally require the apple to be mashed and macerated into a solution for measure-
ment, which causes damage to the fruit and takes a long time with low efficiency, thus they are
not suitable for non-destructive apple grading.
When near-infrared light is directed at a suitable angle to the apple, the chemical groups
within the apple absorb the spectral energy and produce a diffuse reflection on the surface, the
intensity of which varies according to the degree of light absorption within the apple. Apple
sugars contain mainly C-H and O-H groups, which are located at different energy levels and
absorb different energies from different wavelengths of the spectrum. Additionally, these are
selective in their absorption of light, with the spectrum being absorbed only at specific
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frequencies. Therefore, apple sugars can produce characteristic absorption of near-infrared
(NIR) radiation, and the difference in absorption at different wavelengths also affects the
reflected energy of diffuse reflection [17]. Therefore, in this study, near-infrared spectroscopy
technology was used to collect the spectral reflectance data from apple samples within the 400
nm~1000 nm wavelength region. And three chemometrics methods, PLS algorithm,
CARS-PLS algorithm, and SPA-PLS algorithm, were used to build the mapping relationship
between the spectral reflectance data and the apple Brix. Three methods were compared and
analyzed to select the optimal method to measure the apple Brix.
3.2.1 PLS. PLS algorithm is a linear regression algorithm, not affected by the dimension,
and suitable for complex near-infrared spectral analysis. PLS algorithm can effectively elimi-
nate the collinearity of wavelengths. If the model contains a large number of useless informa-
tion variables, the prediction ability of PLS will also be affected [18]. In this study, the PLS
algorithm was used to establish a prediction model between the spectral data of the whole
band and apple Brix.
3.2.2 CARS-PLS. CARS algorithm is a feature variable selection method combining
Monte Carlo sampling and PLS model regression coefficient, imitating the principle of "sur-
vival of the fittest" in Darwin’s theory. Each time, wavelength points with the large absolute
weight of regression coefficients in the PLS model were retained as new subsets through adap-
tive weighted sampling, and then the PLS model was established based on the new subsets.
After multiple iterative calculations, the subset with the minimum root mean square error of
cross-validation (RMSECV) was selected as characteristic variables [19]. In this study, charac-
teristic variables selected by the CARS algorithm and actual Brix of apples were used to estab-
lish a PLS linear regression model.
3.2.3 SPA-PLS. SPA is an algorithm that uses a forward cycle to screen variables, and the
extracted feature bands have low collinearity, which effectively avoids information overlap and
reduces the amount of calculation [20]. The SPA algorithm was also adopted to screen charac-
teristic variables to optimize the effect of the PLS algorithm on predicting apple Brix in this
study.
4 Classifier construction for apple appearance quality detection
4.1 Apple image capture
The vision-based apple appearance detection device, shown in Fig 2, consists of a ring light
source (2835–120, Transcend, Shenzhen), two cameras (HF899, Jereh Microcom, Shenzhen),
a photoelectric switch (E3F-DS30C4, Hutron, Shanghai), and a dark box housing made of
polymethyl methacrylate (PMMA). Among them, the ring light source is adhered to the upper
panel of the housing to provide a light environment, and the top camera is mounted right in
the middle of the ring light source and connected to the universal serial bus (USB) port of an
external personal computer (PC) (HUAWEI MateBook 14, Huawei Technologies Co Ltd,
Shenzhen) through a predetermined external hole. The side-mounted photoelectric switch is
used to detect the passage of fruits on the conveyor belt, and two cameras take pictures when
apples are passed.
4.2 Image preprocessing
Acquiring and processing images of Red Fuji apples in Luochuan, Shaanxi Province, and
extracting and analyzing surface feature data such as the defects, size, and shape of the apples
are types of image preprocessing, and this algorithm flow is shown in Fig 3. First, the camera
was used to directly acquire the original image of the apple; then, preprocessing methods such
as image enhancement, filtering, and morphological processing were used to eliminate
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background interference and improve the accuracy of segmentation; next, the apple surface
color was selected as the distinguishing feature for segmentation of the apple fruit from the
background.
The apple image preprocessing process is shown in Fig 4. The grey level of the original image
shown in Fig 4(A) is corrected using standard color plates and the contrast of the image is
enhanced using the gamma transform [21], as shown in Fig 4(B). Then, the image noise is sup-
pressed by using Gaussian filtering [22], preserving important information such as image con-
tours and edges, as shown in Fig 4(C). Next, the red, green, and blue (RGB) image is transformed
into the hue, saturation, and value (HSV) image shown in Fig 4(D), and the grey level trans-
formed and threshold segmentation [23] were performed to distinguish the white background
from the fruit area, as shown in Fig 4(E). Morphological processing was used to eliminate the tiny
discrete closed area and boundary interference [24] in the white background, as shown in Fig 4
(F). The interior of the highlighted area, where the fruit is located, may also be mistaken for the
black area due to the fruit tip as well as surface reflections. Multiple contours can be found by
finding the boundary pixel mutation points between the highlighted area and the black area. The
Fig 2. Sketch of the structure of the vision-based detection device. 1-ring light source 2-camera 3-photoelectric
switch 4-dark box housing.
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largest contour is extracted as the apple contour, as shown in Fig 4(G). The pixel points at the
edge of the contour are selected to draw the apple contour, as shown in Fig 4(H).
4.3 Defect determination
Apple surface defect types mainly include rot, scars, wormholes, and crush injuries. The defec-
tive area can be extracted by grey level transformation and threshold segmentation of the
Fig 3. Image processing flow.
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preprocessed apple image. However, the national standard does not give a clear definition. To
obtain the size of the area threshold for defect determination, based on the sensory judgment
and the market research, the minimum diameter of the defective area for the apple with a 70
mm medium diameter is 3 mm, and the critical defect ratio for the apple surface defect is
defined t
0
t0¼pd2
0
pd2
1100%¼32
702¼0:18%ð1Þ
If the defective pixel ratio tis greater than or equal to t
0
, the apple is evaluated to be defec-
tive; otherwise, it is evaluated to be normal, and the classifier for detecting apple defects is
t¼00:0018;Normal apples
0:0018;Defective apples ð2Þ
(
Fig 5 shows the image processing process of defect determination. Forty apples were col-
lected, half of them were defective and the other half were not, and the experiment was con-
ducted in triplicate with the use of the defect classifier. The results are shown in Table 1, and
the average accuracy of the defect classifier was 96.67%, indicating that this model can be used
in the grading detection of apple defects.
Fig 4. Image preprocessing process. (a) Original image (b) Gamma transform (c) Gaussian filtering (d) HSV transform (e) Grey level transform (f)
Morphological processing (g) Find contours (h) Drawing contours.
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4.4 Shape determination
From the top view, the shape of the apple was roughly classified into round and oval, as shown
in Fig 6, where (a) to (c) are the contours of nearly round apples and (d) to (f) are those of
nearly oval apples. Customers usually have a higher preference for round apples than for mis-
shapen apples. According to the national standard for grading apples and the consumption
habits, the apple images were preprocessed to extract the maximum contours and calculate the
maximum distance ratio between their horizontal and vertical directions, that is, the aspect
ratio λ[25], to construct the basic parameters of the apple contour classifier. The near round
contours in Fig 6(A) to 6(C) have aspect ratios λranging from 0.98 to 1.05, while the nearly
oval contours in Fig 6(D) to 6(F) have aspect ratios λgreater than or equal to 1.05 or less than
0.98. The apple aspect ratio λcan be used to evaluate the apple shape and judge its general con-
tour. The classifier to detect the apple contour is
l¼0:98 1:05;nearly round apples
0:98or 1:05;nearly oval apples ð3Þ
(
Forty apples were collected, half of them were nearly round and the other half were nearly
oval, and the experiment was conducted in triplicate with the use of the shape classifier. The
results are shown in Table 2, and the average accuracy of the shape classifier was 95%, indicat-
ing that the model can be used in the grading detection of apple fruit shape.
4.5 Size determination
The size of an apple is related to the maximum cross-sectional diameter of the fruit, so the
actual cross-sectional diameter of the apple and the maximum cross-sectional diameter in the
Fig 5. Process of extracting defective area of the apple. (a) Original image (b) Grey level transform (c) Defective apple area (d) Whole apple area.
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Table 1. Results of detection for apple defects.
Testing index Detection number First Second Third
Defective apples 20 20 20 18
Non-defective apples 20 20 19 19
Error 0 1 3
Accuracy 100% 97.5% 92.5%
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image were measured to construct a mapping relationship between the image size and the
actual size of the fruit. Then, the grading system measured the maximum cross-sectional diam-
eter in the image of the apple to be graded, and based on the mapping relationship between the
actual size and the image size, the maximum cross-sectional diameter of the apple was approxi-
mated and used to distinguish among extra-large, large, medium and small apples to aid in the
system to grading.
In this research, we used a vernier caliper to measure and record the maximum cross-sec-
tional diameter W
r
of a sample of apples, and Hu moments to measure the cross-sectional
Fig 6. Typical contours of apples. (a)~(c) Nearly round contour (d)~(f) Nearly oval contour.
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Table 2. Results of detection for apple shape.
Testing index Detection number First Second Third
Nearly round apples 20 19 18 19
Nearly oval apples 20 20 19 19
Error 1 3 2
Accuracy 97.5% 92.5% 95.0%
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diameter W
p
in the image. To measure the cross-sectional diameter of an apple in its image, it
is important to find the center of the apple profile (x
c
,y
c
) and the boundary point of the profile
(x
k
,y
k
) in the image.
Hu moments are geometric moments that characterize an image [26]. When the image
function is f(x,y) and the resolution is U×V, the p+qorder moments of the image is
mpq ¼X
V
y¼1X
U
x¼1
xpyqfðx;yÞ ð4Þ
The 0th order moments of a binary image m
00
represent the area of the contour’s connected
domain. Using the 0th and 1st order moments, the coordinates of the apple contour center
point [27]
xc¼m10
m00
yc¼m01
m00
ð5Þ
8
>
>
<
>
>
:
The radius sequence of (x
c
,y
c
) to that of the boundary point (x
k
,y
k
) in the image is
rk¼ ½ðxkxcÞ2þ ðykycÞ21
2ð6Þ
For the extracted fruit contour in Fig 4(G), the coordinates of its geometric center can be
obtained using Eq (5), and the radius sequence from the geometric center to the boundary
point of the apple contour in the image can be calculated using Eq (6). By using the method of
the least convex package, the maximum R
max
in the radius sequence from (x
c
,y
c
) to (x
k
,y
k
)
was filtered out [28], and the maximum cross-sectional diameter of the apple in the image was
W
p
= 2R
max
.
We selected 150 apple samples and used a vernier caliper to measure and record their maxi-
mum cross-sectional diameter W
r
. Then, we took photos and carried out the image processing
shown in Fig 7. The maximum outer circle diameter of the apple image was obtained based on
the minimum convex hull method, and it was taken as the maximum cross-sectional diameter
W
p
of the apple image. Thus, a linear regression model between W
r
and W
p
was constructed.
The results are shown in Fig 8, and the regression equation is
Wr¼0:4052Wpþ13:5015 ð7Þ
where the correlation coefficient R
2
= 0.8462, the residual variance S= 3.9806, and the test of
significance of variance p<0.001, demonstrating that the linear regression model has a high
degree of accuracy and can meet the experimental requirements.
According to the national standard of the People’s Republic of China, the thresholds of
cross-sectional diameter Wr for distinguishing among extra-large, large, medium, and small
apples are 8 cm, 7 cm, and 6.5 cm respectively. Substituting these values into Eq (7), it can be
seen that the thresholds for apple size grading according to the maximum cross-sectional
diameter Wp of the apple image are 164, 139, and 127 pixels respectively, based on which the
classifier for apple size grading was constructed, as shown in Eq (8).
Wp¼
164;extra large apples
139 Wp;164;large apples
127 Wp;139;medium apples
<127;small apples
ð8Þ
8
>
>
>
>
<
>
>
>
>
:
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Fig 7. The extraction process of contours of apples with different sizes. (a) The large apple (b) HSV transform of the large apple (c) Contour
extraction of the large apple (d) The medium apple (e) HSV transform of the medium apple (f) Contour extraction of the medium apple (g) The small
apple (h) HSV transform of the small apple (i) Contour extraction of the small apple.
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Fifty extra-large apples, 50 large apples, 50 medium apples, and 50 small apples were
obtained, photographed, and after image processing, the cross-sectional diameter W
p
values of
these apples in images were calculated separately and substituted into the classifier for apple
size grading. The experiment was conducted in triplicate, and the results are shown in Table 3.
Statistics show that the average accuracy of grading apples for extra-large, large, medium, and
Fig 8. Linear regression plot of the apple size characteristic.
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Table 3. Results of detection for apple sizes.
Testing index Detection number First Second Third
Extra-large apples 50 48 47 48
Large apples 50 46 48 49
Medium apples 50 46 49 47
Small apples 50 48 46 46
Error 12 10 10
Accuracy 94.00% 95.00% 95.00%
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small apples according to this classifier was 94.67%, indicating that the model can be used for
the grading detection of apple size feature extraction.
5 Apple Brix prediction model construction
5.1 Near-infrared spectral image acquisition device
A near-infrared spectroscopy-based apple Brix detection device was constructed, as shown in
Fig 9, mainly consisting of a spectrometer (USB2000+, Ocean Optics, USA), a tungsten-halo-
gen light source (CH-20001, Changhui Electronic Technology Co., Ltd., Guangzhou), a fiber
optic probe and probe holder (Changhui Electronic Technology Co., Ltd., Guangzhou), a
focusing lens (Changhui Electronic Technology Co., Ltd., Guangzhou) and a dark
box housing (PMMA). The acquisition device consists of two layers of the dark box structure.
The upper layer was used to house the instrument and power supply and the lower layer
detected the internal quality of the apples. The tungsten-halogen light source was arranged
symmetrically at 45˚ on the bottom plate of the lower layer, and a 24V lithium battery (Laiyue
Fig 9. Sketch of the structure of the apple internal quality detection device. 1- Spectrometer 2- Fiber optic probe 3-
Tungsten-halogen light source 4- Focusing lens 5- Dark box housing.
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Electronic Technology Co., Ltd., Guangzhou) was used to supply power to the tungsten-halo-
gen light source.
5.2 Spectral curve acquisition and preprocessing
The surfaces of 168 apple samples were cleaned, and then the spectral curves of the apples were
acquired by connecting the near-infrared spectrometer to a computer through a USB port and
using spectral image acquisition software (Spectra Suite, 2020). The integration time of the
fiber optic spectrometer was adjusted to 4 ms, the averaging time to 30, and the smoothing
degree to 5. In this way, the final spectral reflectance curves of apple samples in the wavelength
range of 400 nm~1000 nm were acquired. The black and white corrections were then carried
out on the acquired curves according to Eq (9) to reduce the influence of the experimental
environment on the results [29].
R¼IId
IwIdð9Þ
Where: R—the spectral reflectance of the sample, %;
I—the intensity of the reflection spectrum of the sample, cd;
I
w
—the reflected spectral intensity of a standard whiteboard (reflectance of approximately
95%), cd; and
I
d
—Reflected spectral intensity in the dark, cd.
A total of 1771 points were collected in the wavelength range between 400 nm and 1000
nm. To avoid additional noise interference, one wavelength point was selected every two
points, and 591 wavelength points were finally selected. The spectral reflectance data of 591
wavelength points of the apple samples were used as the final data for the next step of process-
ing. The multiple scattering correction (MSC) and standard normal variable (SNV) transfor-
mation methods were used to preprocess [30] spectral reflectance data of 168 samples to
reduce the influence exerted by extraneous factors such as noise, equipment, and the experi-
mental environment. The results are shown in Fig 10.
5.3 Apple Brix measurement
After obtaining the spectral data of the apples, the true value of apple Brix was measured using
a handheld digital sugar meter (PAL-1 digital sugar meter, ATAGO, Japan). Each apple was
peeled, diced, and mashed, and the juice was extracted to measure the apple Brix value. After
each use of the sugar meter, the sampling tank was cleaned, rezeroed, and calibrated with
water and then wiped clean with a dry paper towel, followed by the next measurement. The
degree Brix values of 168 apple samples, as shown in Table 4, ranged from 8.9% to 14.6%, with
a sample mean of 11.7%. Analysis of the experimental statistics showed that 90% of the apple
samples had a Brix of 10% and above, and 15% of the apple samples had a Brix of 13% and
above. Therefore, Brix values of 10% and 13% were selected as the thresholds for grading and
dividing the Red Fuji apples in Luochuan, Shaanxi Province, into high, medium, and low
grades based on their Brix values, with the grading criteria shown in Eq (10).
Apple Brix ¼13%;high
10%Brix <13%;medium
<10%;low
ð10Þ
8
>
<
>
:
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5.4 Characteristic wavelengths extraction
The competitive adaptive reweighted sampling (CARS) algorithm and the successive projec-
tions algorithm (SPA) were used to filter the characteristic wavelength points related to the
Brix to eliminate the irrelevant spectral data, reduce the computational effort and reduce the
modeling time [31–33]. Fig 11 shows the results of the characteristic wavelengths selection
using CARS, (a) the variation in the number of variables when different sampling times were
selected, (b) the variation in the root mean square of cross-validation with the number of sam-
pling times, and (c) a graph of the selection results of characteristic wavelengths using CARS.
When the number of sampling times is 49 and the number of retained wavelength variables is
37, RMSECV reached a minimum value of 0.4869.
Fig 10. Spectral curves of samples after preprocessing.
https://doi.org/10.1371/journal.pone.0271352.g010
Table 4. Statistics on apple Brix.
Sample Number Minimum Maximum Average Standard Deviation
168 8.9 14.6 11.7 1.1
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The range of the number of characteristic wavelengths was set from 30 to 50, the SPA algo-
rithm was used to extract the characteristic wavelengths, and the number of wavelengths corre-
sponding to the smallest RMSE was analyzed and calculated to obtain the optimal number of
wavelengths, as shown in Fig 12, a graph of the number of characteristic wavelengths extracted
by SPA and the RMSE. When the SPA algorithm extracted 32 characteristic wavelengths, as
shown in Fig 13, the RMSE reached a minimum value of 0.5562.
5.5 Brix prediction model construction
Using the sample set partitioning based on joint x-y distance (SPXY) algorithm, 168 samples
were divided into a validation set and a prediction set at a ratio of 3:1, with 126 samples in the
former and 42 in the latter. The SPXY algorithm can achieve a uniform distribution space to
ensure the validation set of samples [34], as shown in Eq (11)
dxyðp;qÞ ¼ dxðp;qÞ
maxp;q2½1;Ndxðp;qÞþdyðp;qÞ
maxp;q2½1;Ndyðp;qÞp;q2 ½1;N ð11Þ
The performances of the three models were compared and analyzed as shown in Table 5,
using the PLS algorithm [35] on the raw spectra, the spectra extracted by the SPA algorithm,
and those extracted by the CARS algorithm. The comparative analysis of performances is
shown in Table 5. After modeling with the PLS algorithm, the correlation coefficient of the
prediction set R
p
= 0.6543 for the original spectral data, and the root mean square error
RMSEP = 0.7976, which indicated a large deviation. For the prediction model by extracting
characteristic wavelengths using the SPA algorithm, the correlation coefficient of the predic-
tion set R
p
increased by 0.1787, and the RMSEP decreased by 0.2503. The correlation coeffi-
cient R
p
of the prediction set obtained by extracting characteristic wavelengths using the
CARS algorithm increased by 0.2926, and RMSEP decreased by 0.4461. The scatter plot of the
predicted Brix results for the prediction set is shown in Fig 14, with the horizontal coordinates
representing the true Brix values and the vertical coordinates representing the predicted Brix
Fig 11. Graph of CARS characteristic wavelengths selection results.
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values. The better the fit of the scatter plot to the straight line y=x, the more reliable the pre-
dictions are and the closer the predicted values are to the real values.
Comparing the four model evaluation index parameters, namely the correction set correla-
tion coefficient R
c
, the correction set root mean square error RMSEC, the prediction set corre-
lation coefficient R
p
and the prediction set root mean square error RMSEP, it can be
concluded that the Brix prediction model built using the CARS-PLS algorithm has optimal val-
idation and prediction performance. A comparison of the three scatter plots of the Brix predic-
tion results of the prediction set also yielded the same conclusion.
6 Experimental validation
6.1 Integrated apple grader design
Based on the above analysis, the apple grade was determined comprehensively based on differ-
ent characteristics in terms of defects, shape, size, and Brix of the apple fruit. The algorithm
flow of the apple grading is shown in Fig 15. First, the defects and shape of the apples are
extracted to evaluate whether they meet the requirements. As long as defects exist, the apples
are evaluated as substandard fruits. Among the apples meeting the requirements, those with
high Brix values are evaluated as excellent fruits, those with medium Brix values are the first-
class fruits, and those with low Brix values are second-class fruits; those with no defects but
Fig 12. Number of SPA extracted characteristic wavelengths vs. RMSE curve.
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average shape are directly evaluated as the second-class fruits. Finally, the size features of the
apple images are extracted to achieve the grading of extra-large, large, medium, and small
apples.
6.2 Setting up the experimental platform
To verify the feasibility of the grading algorithm, the prototype apple quality detection system
shown in Fig 16 was set up. When an apple passes on the conveyor mechanism, a position
detection photoelectric sensor in the vision-based detection box sends a low level to the PC
device, which triggers the camera to capture the apple image. The device mainly realizes the
detection of the parameters of defects, contour, and size of the apples. After the detection of
appearance quality, the apples are sent to the internal quality detection system, and the col-
lected spectral information is brought into the Brix prediction model to obtain the predicted
values of the Brix. Finally, the developed upper computer software reflects the internal and
external quality information and the corresponding parameters on the interface and inputs the
detected parameters into the classifier models to assess the comprehensive grading of the
apples and display it on the interface.
Fig 13. Map of SPA extracted characteristic wavelengths.
https://doi.org/10.1371/journal.pone.0271352.g013
Table 5. Comparative analysis of PLS model performances.
Algorithm Characteristic wavelength number Optimum PCs R
c
RMSEC R
p
RMSEP
Original-PLS 591 15 0.9227 0.4486 0.6543 0.7976
SPA-PLS 32 10 0.8680 0.5779 0.8330 0.5473
CARS-PLS 37 14 0.9563 0.3403 0.9469 0.3515
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PyCharm Community Edition 2020 software was used to write the upper computer soft-
ware under Windows, and PyQt5, a module of Python, was used to create the interface for
apple grading detection. The interface for running this software is shown in Fig 17.
6.3 Experimental results
A total of 120 apples were randomly selected to validate the grading system and the results of
the apple grading were output on the interface of the upper computer software. To allow for
optimal image acquisition with the camera, the calyx side of the apple was uniformly placed
towards the conveyor belt, and the stalk side was placed towards the camera on top of the
detection box. The experiment was conducted in triplicate, and the grading results are shown
in Table 6. The accuracy of apple grading detection was 95.83%, 97.50%, and 96.67%, respec-
tively, with an average accuracy of 96.67%.
7 Summary and discussion
This paper analysed the principles of apple defects, shape, size, and Brix detection and grading
based on China’s current national standard. In this research, Red Fuji apples in Luochuan,
Shaanxi Province, were used as the research object, the appearance quality classifier models of
apple defects, shape, and size characteristics were established based on machine vision, and the
prediction model of apple Brix value was constructed based on near-infrared spectroscopy.
The results showed that the average accuracy of apple defects, shape, and size characteristics
Fig 14. Scatter plots of the prediction results (in ˚ Brix). a. Original-PLS b. SPA-PLS c. CARS-PLS.
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grading detection was 96.67%, 95.00%, and 94.67%, respectively. Among the three Brix predic-
tion models, Original-PLS, SPA-PLS, and CARS-PLS, the CARS-PLS model had the best pre-
diction performance, achieving the following performance merits: R
c
= 0.9563,
RMSEC = 0.3403, R
p
= 0.9469 and RMSEP = 0.3515. The validation experiment of the grading
detection platform showed that the average accuracy of the grading detection was 96.67%,
indicating that the apple grading system designed in this paper was feasible to some extent.
Fig 15. Flow chart of the apple quality grading algorithm.
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Fig 16. The prototype of the apple quality detection system.
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The accuracy of apple grading with this method can be further improved by improving the
sensitivity of the photoelectric sensor and the operational stability of the transmission plat-
form, both of which can reduce the position shift when the image is taken so that the apple is
Fig 17. The interface of the apple quality grading software.
https://doi.org/10.1371/journal.pone.0271352.g017
Table 6. The results of apple quality grading according to the national standard.
Grade Detection number First Second Third
Excellent apples 21 19 19 20
First-class apples 35 33 35 34
Second-class apples 49 48 49 47
Substandard apples 15 15 14 15
Error 5 3 4
Accuracy 95.83% 97.50% 96.67%
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centered in the middle of the photo and the defects, shape and size characteristics can be
extracted more accurately. If the distance between the fibre optic probe and the apple can be
automatically adjusted according to the apple size, or the sealing of the near-infrared spectros-
copy inspection device can be improved to reduce the interference of natural light on the spec-
tral data acquisition, the accuracy of the Brix prediction of the system can be further enhanced.
According to the research ideas in this paper, with improvements in some algorithms, the
apple grading detection system constructed can also be applied to the internal and external
quality detection and grading of other nearly round fruits and vegetables such as pears,
peaches, and tomatoes.
Supporting information
S1 File. The list of the grading detection device.
(DOCX)
S2 File. Original data.
(XLSX)
S3 File. Funding information.
(TXT)
Acknowledgments
The authors are grateful to the editors and reviewers for their helpful comments and recom-
mendations, which make the presentation better.
Author Contributions
Conceptualization: Li Liu, Yinggang Shi.
Data curation: Jin Wang.
Funding acquisition: Li Liu, Yinggang Shi.
Investigation: Jin Wang, Yujia Huo, Yutong Wang, Haoyu Zhao.
Methodology: Jin Wang, Li Liu, Yinggang Shi.
Software: Jin Wang, Yujia Huo.
Validation: Jin Wang, Yujia Huo, Yutong Wang, Haoyu Zhao.
Writing – original draft: Jin Wang.
Writing – review & editing: Kai Li, Yinggang Shi.
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