Open Journal of Applied Sciences, 2013, 3, 27-31
Published Online March 2013 (
Copyright © 2013 SciRes. OJAppS
Recognition of Greenhouse Cucumber Disease Based on
Image Processing Technology
Dong Pixia, Wang Xiangdong
College of information science and engineering, ShenYang University of Tchnology, Shenyang 110870
Email: {*Andrew.higgins, Leonie.pearson, Luis.laredo}@csir
Received 2012
This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image
processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of
cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters
of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance
methods to identify diseases of images. The experimental result showed that the current method on disease recognition
accuracy rates more than 96%.
Keywords: Image Processing Cucumber Diseases Shape Features Color Features GLCM
1. Introduction
Study on computer image processing technology began in
the 1960 of the 20th century, applied to the production
and processing of agricultural products began in the 1970
of the 20th century [1].Automatic diagnosis and identifi-
cation of pests, have been the big problem is related to the
growth of crops in agrarian production. Scholars have
also done a lot of research [2][3].2011 Yuan[4] for crop
disease leaves an image with complex background leaf
extracts in question, suggested a level set model based on
prior information, on the segmentation accuracy will im-
prove the speed of evolution and not be a good improve-
ment. 2006 Youwen Tian [5], the application of computer
image processing technology and support vector machine
classification method to study the corn leaf disease identi-
fication. Bingqi Chen in 2009 [6] wavelet transform and
texture matrix stressed wheat disease site. Calculate the
color of the parts of the characteristic value of the disease;
minimum principle color characteristic value difference
between the disease image to be tested and inventory dis-
ease image retrieves inventory disease image.
The application of image processing technology in
greenhouse agriculture greenhouses, refined and intelli-
gent in favour of realizing China's agriculture. Applica-
tion of image processing technology to identify crop
diseases technicians level while avoiding the cause of
disease outbreaks as a result of miscalculations, on the
other hand more rational control of water, fertilizer, me-
dicines, conducive to the sustainable development of the
ecological environment. Crop leaves often earlier reflect
the disease, this choice of cucumber diseased leaves from
the Internet as a research object. Matlab r2010a envi-
ronment for cucumber leaf images for image processing
and recognition [7].
Figure 1 Overall flow chart
Lesion segmentation
Ima ge Prep rocess
Feature extraction
Copyright © 2013 SciRes. OJAppS
2. Image Preprocessing
2.1. Graying
Gathered in this study is color images so that you can
maximize the guarantee the integrity of the image. But
color image storage occupies a larger space, in order to
simplify the image we first gray level transformation.
Grayscale is RGB color image of the same three compo-
nents, a special image. Three RGB grayscale is such a
distinctive component of the same color images. Wherein
one pixel point change in the range between 0 ~ 255, the
same as the description of the color image of the gray
scale image nonetheless reflects the entire image as a
whole, and the local distribution and characteristics of the
chrominance and luminance level. Here we use the
weighted average method gray scale image:
f(i,j )= 0 .3 0R(i,j)+0.5 9 G(i.j) +0 .11B( i.j ) .
Due to the varying range of the gray image, in order to
better handling and identification with the image grays-
cale-adjustment, adjust the gray scale uniform into the
scope of the original image to 0~255 range.
2.2. Image Smoothing
As the image acquisition to the varying quality, in order
to improve the image quality and the processing effect,
we conducted a smooth image. Image smoothing method
commonly used neighborhood average and median filter-
ing method. Neighborhood average method is simple and
fast, but at the same time easy to cause the edges of the
image blurred. The median filtering method compared to
the neighborhood average has a greater advantage in the
extraction of edge. So this article using median filtering
2.3. Lesion Segmentation
For each disease, we choose ten images to explore their
lesion site RGB range. Finally we found powdery mildew
R(150-200 )G(15 0 -200)B(150 -2 00);do wny mildew
R(140-190 )G(14 0 -200)B(40 -120);Anthracnose
R(150-230 )G (160-200)B( 100 -140).This range is compa-
ratively rough, to satisfy the above range when the pixel
values in the image, we will be extracted, and then use the
expansion, the operation of the corrosion lesion site. Le-
sion segmentation results are shown in the following fig-
Figur e 2. From left to right followed by downy mildew, powdery mildew, anthracnose lesion segmentation map.
3. Feature Extraction
3.1. Morphological Feature Extraction
Through disease spot parts of mark for a disease spot
complexity, roundness, long axis ratio, degree of rectan-
gular these four shape characteristic value.
The complex shape of the lesion degree discrete index
e, where S represents the area, L lesion circumference.
the unit area of the lesion, perimeter bigger then the
greater the value of e, the lesion more complex the shape,
i.e. discrete graphics, whereas simply represents graph-
Copyright © 2013 SciRes OJAppS
=4 /C SL
Roundness, reflect the degree of the lesion area close
to the circle. Hence we know C is 0~1. C values closer to
1 lesion area and more nearly circular.
Major to minor axis ratio
Ratio of the length of the major axis length and the
minor axis of the ellipse with the region having the same
criteria, the second-order central moments.
Re /
bounding box
c SS
The rectangle ratio is a lesion area (area) with the le-
sion minimum bounding box (bounding box) of the ratio
of the area of the reaction to the extent of the lesion ap-
proximately rectangular.
An image of the above four values in turn calculating
mean, a total of four shape characteristic parameter, as
diseases identification parameter value.
3.2. Color feature extraction
The leaf color change is a very intuitive reaction of crop
diseases. Simply rely on shape features are not sufficient
to accurately identify the disease, after joining the color
characteristics conducive to improving the accuracy of
judgment and accuracy.
Here we extract the pixel values of the lesion area
R/G/B, and the average value of the entire lesion area
R/G/B as the final color feature parameters.
3.3. Texture feature extraction
Texture reaction of the pixel space distribution properties,
it usually appears as local irregular and macroscopic reg-
ular characteristics. At present the extraction methods of
texture feature are numerous. We adopted the gray level
co-occurrence matrix method.
The joint probability density of the two positions of
pixels to define the GLCM.GLCM of an image can re-
flect the image gray scale of direction, adjacent interval
variations. It is to analyze the image of the local mode
and their arrangement rules of the foundation
GLCM statistical spatial having a positional relation-
ship of a pair of pixel gray scale on the frequency of the
occurrence. Its substance from the image gradation is I
am starting (position x, y), statistics distance is d, the
gradation of j pixel (x + Dx, Y + Dy) occur at the same
time the frequency of P (I, j, d,). The mathematical ex-
pressi on:
P(i,j, d,)
{(, ),(+,)|(, ),(,)}x yxDx yDyfx yif xDx yDyj
+=+ +=
In the above formula:
x,y=0,1,2,N-1 Is the image pixel coordinates;
i,j=0,1,…L-1 Is the gray level
Dx,Dy Is the position offset
d is the w matrix build step
θ is the direction of the W matrix of generating, 0 °,
45 °, 90 ° and 135 °, directions.
For a L-level gray scale M * N image shows that the
GLCM matrix L * L. L generally takes 8 or 16. But the
gradation of the image is generally in the range of 256.
So here we are with their gray scale compression, com-
pressed to 16 and GLCM normalized.
The GLCM generated according to the above formula
(1) is a symmetric matrix. If the texture is rough, the P
matrix is not 0 elements will be concentrated in the vi-
cinity of the main diagonal, the detailed texture P array
rather scattered distribution of the elements. If the θ di-
rection consistent with the grain, then p elements in the
array are concentrated near the main diagonal. We
choose the texture matrix energy, entropy, contrast the
three characteristic parameters identification of mean and
variance as the disease's characteristic parameters.
(,) ;
ASMP ij=∑∑
Energy reflects the image gray scale distribution un-
iformity degree and grain weight degree. If the image
gray distribution is more homogeneous and the ASM
value small; On the contrary, if uneven distribution of
gray, the ASM value big. One of the conditions is sym-
biosis matrix element concentration distribution, ASM
value at large. ASM value big shows that a more uniform
and rule change texture model.
( ,)log( ,);
ENTPij ij= −
Entropy is a measure of the image with the amount of
information; texture information may also belong to the
image information, randomness metric, the symbiotic
matrix element dispersion distribution, large entropy. It
says the image texture in the heterogeneous degree or
()( ,);
CONijP ij= −
The contrast reflects the degree of image sharpness
and texture of deep grooves. Texture grooves deeper the
greater the contrast, the sharper visual effects; conversely,
the contrast is small, when the groove is shallow, the
effect is unclear.
GLCM determined lesion site and calculate these three
parameters in the 0 °, 45 °, 90 ° and 135 ° directions on
the value, the mean and variance of energy, entropy and
contrast of six quantities as the final texture characteristic
Copyright © 2013 SciRes. OJAppS
4. Disease Identification Based on Minimum
This study was extracted from 13 characteristic parame-
ters, due to the larger data sample value ranges so we will
sample characteristic parameters unified normalized. By
comparing and experiment 13 parameters of the results
are significantly affected by the following four characte-
ristic parameters: energy Mean, standard deviation of the
entropy, the rectangularity mean, B mean.
Minimum distance classification is by definition un-
specified sample to various standard center distances,
place samples into the distance the smallest category,
with samples of the various training centre as a standard
centre. Use the shortest distance method, calculate the
characteristic value of disease samples and a standard
center distance between different diseases, due to the
characteristics of each contribution to the recognition
result where we have added weight this, final recognition
more accurate.
1122 33 44
θθθ θ
In the above formula:
is the right value. In this article,
four weights in turn take 0.2,0.15,0.1,0.55 optimal rec-
ognition results are shown in Table 2, the correct rate.
is the distance between the center of
the value and the standard of the sample characteristics.
Calculates the distance between the sample image
feature value and the standard center, respectively, Be
identified diseases with standard diseases are more simi-
lar, the distance will be smaller, so that the sample dis-
eases classified class In the table below 1-25 is downy
mildew sample characteristic value, 26-50 is powdery
mildew sample characteristic value, 51-75 is anthrax
sample characteristic value.
5. Conclusion
This article using image processing and pattern recogni-
tion method for identifying the diseases of greenhouse
cucumber. Found that the use of the median filter, proba-
bly based on the characteristics of the lesion color range
extraction lesion method is very effective. Cucumber
Table 1: characteristics of downy mildew, powdery mildew
and anthracnose
Samples \
devia tion
mean B mean
1 0.0532 0.8381 0.3354 0.4265
2 0.3050 07090 0.1944 0.4396
… … … … …
25 0.3975 0.6039 0.1728 0.5 117
26 0.9827 0.0311 0.7815 0.8 025
27 0.8967 0.1056 0.6148 0.8 305
… … … … …
50 0.7205 0.1559 0.4730 0.7 675
51 0.8820 0.1222 0.7282 0.4 797
52 0.6947 0.2304 0.6279 0.5 905
… … … … …
75 0.6142 0.3238 0.6606 0.4 695
Table2 : identifies the correct rate
name Samples
The correct
downy mildew 25 24 96%
Powdery mildew
25 25 100%
anthracnos e 25 25 100%
downy mildew, powdery mildew and anthracnose signif-
icant correct rate based on texture feature parameters, the
color characteristic parameters and shape feature parame-
ters to identify, to identify the 25 samples of each disease,
the correct rate of more than 96%, fully proved that the
method is feasible.
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