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Probabilistic Neural Network for the Automated Identification of the Harlequin Ladybird (Harmonia Axyridis)


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This paper describes recent work in the UK to automate the identifi-cation of Harlequin ladybird species (Harmonia axyridis) using color images. The automation process involves image processing and the use of probabilistic neural network (PNN) as classifier, with an aim to reduce the number of color images to be examined by entomologists through pre-sorting the images into correct, questionable and incorrect species. Two major sets of features have been extracted: color and geometrical measurements. Experimental results re-vealed more than 75% class match for the identification of taxa with similar-colored spots.
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Probabilistic Neural Network for the Automated
Identification of the Harlequin Ladybird (Harmonia
M. Z. Ayob1 and E. D. Chesmore2
1 Universiti Kuala Lumpur British Malaysian Institute
Gombak, Selangor Malaysia
2 Department of Electronics
University of York
YO10 5DD York, UK
Abstract. This paper describes recent work in the UK to automate the identifi-
cation of Harlequin ladybird species (Harmonia axyridis) using color images.
The automation process involves image processing and the use of probabilistic
neural network (PNN) as classifier, with an aim to reduce the number of color
images to be examined by entomologists through pre-sorting the images into
correct, questionable and incorrect species. Two major sets of features have
been extracted: color and geometrical measurements. Experimental results re-
vealed more than 75% class match for the identification of taxa with similar-
colored spots.
Keywords: automated identification, feature extraction, image processing,
probabilistic neural network
1 Introduction
The use of automated identification of ladybirds using color images has considera-
ble potential for biodiversity monitoring and has not been previously explored. In
spite of the many advantages, automating the identification process is not trivial. Tra-
ditionally, an expert will manually obtain details from the elytra and other features for
species identification. Dichotomous keys have been most commonly used, which are
based on morphological criteria such as color and size. These physical traits are too
small to examine without magnification, considering the size of typical ladybirds
themselves which are around 2-8 mm, and harlequin ladybirds vary around 6-8 mm
[1]. Furthermore, most ladybird species have a variety of color forms and spot pat-
terns. Both intra-species and inter-species variations can be very large, making spe-
cies identification a difficult skill to master. Part of the objectives of this research is to
capture specific features that would make pre-sorting of ladybird species easier. These
factors contribute to the development of an automated system for ladybird identifica-
tion. In this paper the focus is the identification of harlequin ladybirds (Harmonia
axyridis), an alien ladybird species found in the UK.
2 Background
The Harlequin ladybird is a voracious ladybird species that has been invading the
UK since 2004. It feeds on aphids and other native ladybird species [2-3]. The beetle
has now spread to most parts of England and Wales. It is receiving considerable atten-
tion due to its potential impact on ecological and biological balance [4]. To make
things worse, there has been a general decline in the taxonomic workforce which in
effect has been part of the taxonomic impediment to biodiversity studies [5]. In re-
sponse, The UK Ladybird survey is a web-based recording system for the general
public to send in their sightings and photographs. These color images enable the phys-
ical details of the ladybird forewings (called ‘elytra’) to be visually examined through
image processing steps, which will be explained further in subsequent sections.
There are up to 15 described color forms of H. axyridis; however, only three dif-
ferent forms are studied here. Fig. 1 shows three color forms of H. axyridis. It shows
the variation in colors; hence the problem to perform automated identification is
based on color images only. Table 1 contains the physical description, which serves as
an identification guide for harlequin ladybirds in UK. In terms of size and shape, the
Harlequin is generally large and the length is between 6 mm to 8 mm. The pronotum
pattern can be white or cream in color. It can contain up to 5 spots or fused lateral
spots forming 2 curved lines, M-shaped mark or solid trapezoid. The wing cases have
wide keel at the back, and legs are almost always brown.
(a) Form succinea (b) Form conspicua (c) Form spectabilis
Fig. 1. Top-view of the three forms of H. axyridis (Source: CEH Wallingford, UK)
Table 1. Identification guide for H. axyridis f. succinea, f. conspicua and f. spectabilis
Form succinea
Form conspicua
Form spectabilis
Orange with 0 to
21 black spots
Black with 2
red/orange spots,
and inner black
Black with 4
red/orange spots
Round and domed
Round and domed
Round and domed
6-8 mm
6-8 mm
6-8 mm
There are two major development works in this investigation, which are image
processing and intelligent systems. The work in image processing involves two major
processes: greyscale operations and color image processing. Section 3 covers the
concepts and experimental work that has been carried out in image processing. These
operations are implemented in MATLAB 2012 and GIMP2 [6]. Subsequently, the
intelligent system consisting of PNN been implemented in WEKA [7] is elaborated.
Section 4 discusses the results of experiments on PNN, and in Section 5 the paper
concludes with future recommendation.
3 Methodology
3.1 System block diagram
A prototype automated species identification system has been developed to distin-
guish UK ladybird species using image processing and probabilistic neural networks
(PNN). The block diagram is shown in Fig. 2.
Fig. 2. Block diagram of the prototype of an automated ladybird identification system
3.2 Image processing
Based on color variation, the author has initiated investigating the use of color as
the leading feature for identifying ladybird species. Based on visual observation, color
represents the natural characteristic of ladybirds. Ladybirds commonly possess large
variation in body color. Some are quite obvious, for instance, there is a species com-
monly called ‘orange ladybird’ which has orange-colored elytra and sixteen white
spots. There are also ‘striped ladybirds’ with brownish elytra and cream-white stripes.
The sightings can be confirmed based on publications by the UK ladybird identifica-
tion guide, Leicester & Rutland color key and Southampton Natural History Society
[1, 8-9].
In this work, CIELAB color space is used for representing pixel colors obtained
from the elytra and body markings. CIELAB is an approximate uniform color scale to
represent visual difference in the form of color plane, and able to represent chroma
separately from lightness [10]. It is also able to represent the chroma values in the
form of color plane. The maximum values for a* and b* are +120, while minimum
values are -120. The range for L-axis is 0-100. Unlike RGB, CIELAB is also device
CIELAB was selected as the desired color space to reduce illumination problems
since the majority of ladybird images obtained for the study were photographed in
their natural habitat. These images are prone to illumination issue, which is a variable
that is quite difficult to control. By separating lightness and chroma using CIELAB as
the color plane, subsequent work has been made simpler to solve [11-12]. CIELAB is
useful as it distinguishes one specific color to be distinct from another visually similar
color, and the difference between chroma values can be calculated [13]. Hence, errors
between visual perception and actual values due to bad illumination are significantly
Another issue with the images is the background clutter. This is shown in Fig. 3,
where work on the image of a scarce 7-spot ladybird (Coccinella magnifica) without
background clutter revealed elytra markings better than the same image with back-
ground. Based on testing, it has been decided that the use of CIELAB is limited to
capturing pixel values on the elytra and spots. Segmentation and elimination of back-
ground clutter are done in RGB color space. Body marking measurements are per-
formed in greyscale and the image then converted into binary format. Only the colors
of the body markings are captured in CIELAB values.
For each image, CIELAB values are obtained by reading the average L*, a* and b*
values from a user-interactive pixel capture box. The size of the capture box is not
fixed. It varies between 25x25 to 100x100 square pixels depending on the image reso-
lution, hence user and image-dependent. Higher resolution images require a smaller
capture box, and vice versa. If the size of the capture box were fixed, and the image is
of low-resolution then the border pixels become indistinct and edges are difficult to
locate. Once the average values were obtained, each value was normalized to [-1, 1].
Fig. 3. (a) Image of scarce 7-spot (with background) after segmentation showing background
clutter, and (b) same image with clean background
The following formulae were applied for normalization:
 (1)
 (2)
 (3)
Fig. 4 shows the representation of the spot color and elytra/BG color (in CIELAB
values) on normalized color planes. Refer Table 2 for species names and acronyms.
3.3 Geometrical characters extraction
In addition to color, geometrical characters have been utilized for identification. In
order to get geometrical measurements, greyscale and binary image processing were
performed rather than using color image processing as it involves minimal complica-
tions to perform binary processing in one channel. Initially images have been convert-
ed to greyscale via the MATLAB function ‘rgb2gray’ and resized to 640x480 pixels.
The image is then converted to CIELAB for extracting pixel values. Geometrical
measurements on spots and elytra are then performed.
Fig. 4. CIELAB color distributions among local ladybird species and H. axyridis
3.4 Data organization
Three groups of ladybird data are formed: white, red and black. These are named
based on typical ladybird’s spot color. Table 2 shows the grouping and their acronym.
Both UK ladybirds and Harlequins images are used in the experiments.
Table 2. Ladybird groups, species and acronym (in brackets)
Calvia quattuordec-
imguttata Linnaeus
Halyzia sedecimgut-
tata Linnaeus (H16)
Exochomus quadri-
pustulatus Linnaeus
Harmonia axyridis f.
spectabilis Pallas
Harmonia axyridis f.
conspicua Pallas (H2)
Adalia bipunctata
Linnaeus (A2)
Coccinella quinque-
punctata Linnaeus
Coccinella sep-
tempunctata Linnaeus
Harmonia axyridis f.
succinea Pallas (H3)
For the identification of biological species, results are typically presented in terms
of contingency table, or better known as confusion matrix. Based on the formulation
by Bradley [14], the extended metrics used are:
 (4)
 (5)
 (6)
A total of 40 samples per group were used, whereby cross-validation was applied
to all samples. Cross validation is a technique commonly used to compare models, to
estimate accuracy of a classifier and to avoid over fitting [16-18]. By doing so, the
generalisation of a trained classifier is assessed against an independent dataset. A
variant of cross validation is called K-fold cross validation, where the dataset is parti-
tioned into K equal-sized folds and the holdout method is repeated K times [19]. For
each run, one of the folds is used as the test set and the (K-1) remaining folds used for
training. Each of the K folds will be used once as validation data. After K runs, the
average cross validation error across all runs is computed. The error is an estimate of
how the classifier would perform if the data collected is an accurate representation of
the real world.
3.5 Probabilistic neural networks (PNN)
Probabilistic neural networks (PNN) are used as the classifier. A classifier func-
tions to map unlabeled instances to a class label using internal data structures [19].
PNN is chosen as classifier over other techniques because it implements kernel dis-
crimination analysis, meaning the operations are organized into a multilayer feed
forward neural network consisting of input layer, radial basis layer and competitive
layer [20-21]. For a supervised classifier such as PNN to work, it contains exemplars
and targets. The network structure is shown in Fig. 5.
Fig. 5. PNN network structure (Source: MathWorks)
The input layer consists of nodes that receive the data. The radial basis layer con-
tains a probability density function (pdf), using a given set of data points as centers.
The PNN uses normalized Gaussian radial basis functions as a network [22]. The
output layer selects the highest value and determines the class label. In general, a
PNN for M classes is defined as the following [23]:
 (7)
where j = 1,…, M and nj is the number of data points in class j.
A decision boundary is found by finding the numerical solution to the above for
each class. For instance, for a two-class problem this is done by equating y1(x) to y2(x)
and finding solution using grid search [24].
4 Results and discussion
In this work, 10-fold cross validation was applied and the following results were
Table 3. Confusion matrix for C14H16 (white group)
Table 4. Detailed metrics for C14H16 (white group)
TP rate
FP rate
Results obtained at MinStdDev = 0.1, no. of clusters = 2, AUC = area under curve
Table 5. Confusion matrix for E4H1H2 (red group)
Table 6. Detailed metrics for E4H1H2 (red group)
TP rate
FP rate
Results obtained at MinStdDev = 0.1, no. of clusters = 3, AUC = area under curve
Table 7. Confusion matrix for A2C5C7H3 (black group)
Table 8. Detailed metrics for A2C5C7H3 (black group)
TP rate
FP rate
Results obtained at MinStdDev = 0.1, no. of clusters = 4, AUC = area under curve
From the results in Table 3, it has been shown that PNN is able to identify
ladybirds in the white-spotted ladybirds group to 100% accuracy. The fact that they
have been grouped based on their spot colors means PNN is able to completely
discriminate the two species. Next, test results on the red-spotted ladybirds group
reveals 75.83% accuracy. For H1, one instance of misidentification as E4 and eleven
misidentifications as H2. For H2, the 0.575 TP rate means seventeen instances of H1
have been confused as H2.
Table 7 shows 88.75% accuracy for the identification of ladybirds in the black-
spotted group. The TP rate of 0.975 for A2 shows only one misidentification as H3.
This shows that between the two species A2 and H3, there are inherent similarities
which contribute to the low numbers of misidentification. Similar observation can be
said for C7 and H3. Interesting enough for H3, 10 out of 40 instances have been
misidentified as C5 giving only 0.75 TP rate.
Use of CIELAB values to represent spot colors and applying PNN in ladybird iden-
tification are some contributions to knowledge. The observations in this work will be
further investigated in the near future to see any correlations, for instance, to under-
stand which character contributes more towards the identification process. Finding
this ‘contribution factor’ will involve deeper investigations into network model, struc-
ture and the PNN algorithm itself. It will also involve shuffling between taxa to inves-
tigate intra-species and inter-species variations, as experimented by Ayob and
Chesmore using multilayer perceptron (MLP) [25]. At present, it can be said that
PNN works well to identify up to 4 taxa with a minimum of 75.83% accuracy.
5 Conclusion
In short, the use of PNN can be regarded as highly useful in the automated
identification of ladybirds, including alien species. This is supported by experimental
results on three groups of ladybirds involving UK species and Harmonia axyridis,
which have been pre-grouped by their spot colours. Two-species identification of
white-spotted taxa reveals 100% accuracy, while the identification of red-spotted taxa
and black-spotted taxa reveals 75.83% and 88.75% accuracies respectively. Future
investigations will look into the role of characters towards species identification.
The authors would like to extend thanks to Universiti Kuala Lumpur, University of
York and Majlis Amanah Rakyat (MARA) for sponsoring the research work. Special
thanks to Centre for Ecology & Hydrology (CEH) Wallingford, UK for supplying
stock ladybird images.
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... Examples using deep learning classifiers as in [28,31,33] provide good performance but incur in high computational costs as they skip the classification space reduction, for example, sometimes preferring to slide a subwindow into the whole image to attempt any detection or classification task. On the other hand, classifiers based on shallow learning as in [34,35] are less complex but with inferior performances. Manual identification by experts is still used as the reliable approach. ...
... In [40], an SVM classifier with radial basis kernel function was used to identify four species of rice pests on a data set with 156 feature vectors, reaching an ACC score of 97.5%. In [34], a set of color and geometrical features was computed to classify 360 images of ladybird beetles using a probabilistic neural network-based classifier, achieving a mean ACC value of 88.19%. Likewise, the developed method in [35] employed a combination of a multilayer perceptron and a J48 decision tree to classify 9 species of ladybird beetles, obtaining an averaged ACC of 81.93%. ...
... Given the background summarized in this section, it is possible to notice that most previously developed methods were employed to tackle the insect detection and classification problem in a general way. Only a few of them were applied to detect and classify ladybird beetles, but obtaining reduced performance as in [34,35,37] or incurring in a high model cost of classification as in [33]. Therefore, we expect that the contributions of the proposed method address the existing limitations of developing automatic ladybird beetle detection models without a high classification cost and without losing detection performance. ...
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