Bacterial foraging optimization based adaptive neuro fuzzy inference system

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 3568~3575
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp3568-3575  3568
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Bacterial foraging optimization based adaptive neuro fuzzy
inference system
C. Arul Murugan1
, G. Sureshkumar2
, Nithiyananthan Kannan3
, Sunil Thomas4
1
Department of Electronics and Telecommunication Engg, Karpagam College of Engineering, India
2
Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, India
3
Department of Electrical Engineering, Faculty of Engineering, Rabigh, King Abdulaziz University, Saudi Arabia
4
Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, United Arab Emirates
Article Info ABSTRACT
Article history:
Received Aug 19, 2019
Revised Dec 25, 2019
Accepted Jan 11, 2020
Life of human being and animals depend on the environment which is
surrounded by plants. Like human beings, plants also suffer from lot of
diseases. Plant gets affected by completely including leaf, stem, root, fruit
and flower; this affects the normal growth of the plant. Manual identification
and diagnosis of plant diseases is very difficult. This method is costly as well
as time-consuming so it is inefficient to be highly specific. Plant pathology
deals with the progress in developing classification of plant diseases and their
identification. This work clarifies the identification of plant diseases using
leaf images caused by bacteria, viruses and fungus. By this method it
can be identified and control the diseases. To identify the plant leaf
disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed.
The proposed method shows more refined results than the existing works.
Keywords:
Bacteria foraging algorithm
Feature extraction
Image segmentation
Plant disease
Soft computing Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Nithiyananthan Kannan,
Department of Electrical Engineering,
Faculty of Engineering,
King Abdul Aziz University,
Rabigh, Saudi Arabia.
Email: nmajaknap@kau.edu.sa
1. INTRODUCTION
Indian Economy is necessitous in the role of agriculture. India is an agriculture based country,
where more than 50% of population depends on agriculture. Like human being plants also get affected from
diseases, to prevent this farmers must use suitable pesticides for crops. Both the quantity and quality of crops
get affected due to diseases. Visual observation of a plant is done by plant pathology. Computer devices act
as a consequential role in numerous applications such as defense, agriculture, medical and engineering.
Huge number of research has been reported [1-35]. In early days, the expertise person in the field manually
analyses and diagnosis the plant disease which are too difficult to be monitored and also it required more
time. Plant serve as a backbone in all aspects of life, plants also suffer from diseases which affects the normal
growth of the plant.
In this article, it has been presented an automatic soft computing approach named ANFIS to identify
and classify diseases of plant leaves. Image segmentation is done to split the leaf images into smaller parts
for clear identification of affected parts of leaf. Image segmentation is done with the texture, color and shape
of an image. Segmentation is the preprocessing technique which comprises of soft computing and traditional
method. Soft computing is an automatic method which is used for complex task (i.e. image segmentation)
without human knowledge.

Int J Elec & Comp Eng ISSN: 2088-8708 
Bacterial foraging optimization based adaptive neuro fuzzy inference system (C. Arul Murugan)
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2. RELATED WORK
In this technique [36] plant diseases symptoms was identified and it was tested using algorithm
to achieve better accuracy. Conduct an investigation [37] in classification methods to get rid of errors and
also discussed the models to extract information in infected leaf. In [38] proposed a methodology in four
different phases to support accurate differentiation of leaf diseases and classify the detection. Comparison
was made for segmentations using automated tool and leaf symptoms were identified [39]. An inventive
technique elicited from OCM [40] was introduced to analyze the leaf mechanism and the leaf spots provide
stability in the framework of hybrid algorithms. In this work [41] a new approach was considered to emulate
the results and the portions of leaf are combined to address the image analysis. It is widely used to adapt
the segmentation in a general measure of bias. The images were automatically classified [42] in a training set
to facilitate accuracy in deep learning models. Approaches involved have the potential to identify the species
of perspective models. Evaluation was made for running time and accuracy also guidelines were given to
overcome the problems in future research works [43]. In [44], bacterial spot was assessed and also evaluated
the severity of diseases which are inaccurate causing lesions on leaves. Using some modifications visual
methods quantify the potential risk of infection. The diseased areas of each leaf are collected to facilitate
digital scanning. The author proposed an algorithm [45] to identify the results of maize disease in
the detection of plant leaf. A data point in same class is a prime technique to enhance colour analysis in
favor of feature extraction. In this research work [46, 47] depicts a prototype called eAGROBOT to detect
the crop disease.
3. RESEARCH METHOD
In this paper the plant leaf disease is analyzed and predicted by using Adaptive Neuro Fuzzy
Inference System. Basically human beings calculate the characteristics of a particular thing by comparing
the predetermined value obtained by other authors. Similarly system analyses the same predetermined values
to obtain the expected result. Feature extraction is related to dimensionality reduction. With the help of
ANFIS, the proposed algorithm achieves higher accuracy.
4. PLANT DISEASE
Similar to human beings and animals reveal symptoms of suffer from diseases. The entire plant gets
affected by the diseases including flower, root, leaf, stem and fruit. Most of the time it is difficult to identify
the plant under various factors which may cause leaves drop, flower and fruit. Well-defined treatment and
identification of plant diseases is required for growth of certain factors and appropriate diagnosis. The aspects
for diagnosis of diseases depends on
 Glancing for symptoms or signs: the appearance of some unwanted spots, curls and dead areas are visible
to the naked eyes.
 Awareness of the normal characteristics of host plant: it is easier for one to diagnosis the plant disease
if one should know the inheritances and assets of the host plants.
 The occurrences of symptoms: it depends upon two factors (i) the Disorder, (ii) Diseases some
environmental problems lead to disorders, it happens suddenly like within a day or week and does spread
over the parts of the plants.
 Detecting the diseases of the host plant in nature pattern is of two types uniform and non-uniform.
Non-living factors are the cause for uniform pattern whereas non-uniform pattern is caused by some
disease or insect. The disease classification and its effect, symptoms of fungal plant are explained in
Table 1.
Table 1. Fungal plant disease classification
Leaf Image Diseases name Effect
Suitable Climatic
Condition
Symptoms/Cause
Common rust
Roses, hollyhocks,
snapdragons get
affected
Low temperature
Appears primarily on the
surface of lower leaf, reddish
orange spore mass

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3568 - 3575
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Table 1. Fungal plant disease classification (Continue)
Leaf Image Diseases name Effect
Suitable Climatic
Condition
Symptoms/Cause
Late blight
entire plants get
infected
temperature below
70degree Fahrenheit
resemble on the lower, older
leaves as water soaked, grey
green spots
Cedar apple rust infect junipers
spore over winter
appears as a reddish
brown gall
moisturized weather
Leaf curl
reduce fruit
production
temperature above
70degree Fahrenheit
leaf shape changes in to
spring, reddish area on
developing needs
Leaf spot
as spots become
more numerous,
entire leaves may
yellow and leaf drop
will takes place
warm and cool
temperature
Leaf spot, brown blotches on
the leaf
Early blight
stem, fruit and upper
position of the plant
get infected
moderate
temperature
small brown spot appears on
the lower side of the leaves
with concentric rings which
forms a bull-s eye pattern
5. PROPOSED WORK
In the proposed work, it has been spirited on identification and classification of plant diseases using
a few computational and intelligence advance. The perfection research area is emerging for the development
of an automated system for identifying and classifying the adulterated plants with different diseases.
To prevent the qualitative and quantitative loss of agricultural yields identification of diseases is the only
way. The limitation of human vision can be overcome by the accurate and timely detection of diseases with
the help of image processing techniques. The leaf image is pre-processed initially. Using adaptive k-means
diseases part is segmented. Texture and statistical technique are also included to obtain high pass version of
feature extraction.
5.1. Image acquisition
The acquired image is enhanced by using UN sharp filter, by filtering a UN sharp version in dark
image from the original image. It filters the edges. When compared with negative of the Laplacian filter the
parameter alpha is used for unsharp filter and the alpha value is selected as 0.2, by the ‘fspecial’. K-means
method is used for image segmentation and flowchart for the proposed approach is shown below in Figure 1.

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Figure 1. Flowchart for proposed approach
6. RESULTS AND ANALYSIS
The proposed work was implemented on MATLAB 2014a. For justifying the success of this work
we have taken two set of images. First we have taken 6 set of images as an input images which are affected
by diseases caused by fungi and second step we have finally two hundred and seventy images. The affected
region of the leaf part is identified by segmentation of pixels. The proposed work is done successfully with
the accurate value that is the diseases are classified perfectly. For classification the parameters are required
are specificity and Sensitivity. The affected area of leaf part are observed and presented in Table 2 with their
classifications.
Table 2. Results for ANFIS, (a) Original image, (b) Segmented grayscale image, (c) Mask image,
(d) Results for segmented leaf disease
(a) (b) (c) (d)
Common rust

 ISSN: 2088-8708
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Table 2. Results for ANFIS, (a) Original image, (b) Segmented grayscale image, (c) Mask image,
(d) Results for segmented leaf disease (Continue)
(a) (b) (c) (d)
Late blight
Cedar apple rust
Leaf curl
Leaf spot
Early blight
Plant leaf diseases depends on its own feature refers to symptoms. Features like shape, color and
size can be determined by feature extraction. To search seed points and to group them region growing
method is used in feature extraction. For well-defined examination defined the input image is converted into
segmented gray scale image. Specificity and Sensitivity validation parameters used in the proposed work.
Adaptive K-means is used for evaluating the segmentation process. Table 3 shows the report of specificity
for the proposed algorithm along with the comparison of existing algorithms. Table 4 shows the report of
sensitivity for the proposed algorithm along with the comparison of existing algorithms. The pictorial
representation of detected leaf diseases for proposed and existing algorithms with specificity results is shown
in Figure 2. The pictorial representation of detected leaf diseases for proposed and existing algorithms with
sensitivity results is shown in Figure 3.

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Table 3. Comparison of specificity results for detection of leaf diseases
Disease K-means Genetic algorithm BRBFNN ANFIS
Common rust 0.7817 0.8096 0.8213 1.0000
Late blight 0.8014 0.8205 0.8326 0.8571
Cedar apple rust 0.7801 0.7854 0.8196 1.0000
Leaf curl 0.7517 0.7889 0.8879 0.8571
Leaf spot 0.8124 0.8318 0.8836 1.0000
Early blight 0.8211 0.8374 0.8897 0.8571
Average specificity 0.7914 0.8139 0.8558 0.9285
Table 4. Comparison of sensitivity results for detection of leaf disease
Disease k-means Genetic algorithm BRBFNN ANFIS
Common rust 0.8078 0.8117 0.8311 1.0000
Late blight 0.8189 0.8339 0.8497 1.0000
Cedar apple rust 0.7809 0.7996 0.8407 0.8571
Leaf curl 0.7729 0.8279 0.8938 1.0000
Leaf spot 0.8111 0.8315 0.9078 1.0000
Early blight 0.8201 0.8471 0.8999 1.0000
Average sensitivity 0.8020 0.8244 0.8705 1.0000
Figure 2. Inference for specificity interpretations
Figure 3. Inference for sensitivity interpretations
7. CONCLUSION
If human beings get affected by diseases, it has been cured by itself consulting a doctor, but plants
should be observed by human beings to prevent the diseases. But human observation for a prolonged time is
impossible and it is difficult. To overcome this, an automatic approach was designed which helps to monitor
the plant leaves and identify the leaf disease. This automatic approach clearly identifies the disease and
disease can be cured at earlier stage. For identifying the plant leaf diseases ANFIS has been used. While
comparing to the existing algorithm results, the proposed algorithm shows 90 percent results. Proposed work
shows higher accuracy and effectiveness. Proposed work has been successfully done on fungal diseases, in
future it can be expected to work on bacteria and viruses.
0
1
2
Common
rust
Late
blight
Cedar
apple rust
Leaf curl Leaf spot Early
blight
Average
sensitivity
Specificity Results
k-means Genetic algorithm BRBFNN ANFIS
0
1
2
Common
rust
Late
blight
Cedar
apple rust
Leaf curl Leaf spot Early
blight
Average
sensitivity
Sensitivity Results
k-means Genetic algorithm BRBFNN ANFIS

 ISSN: 2088-8708
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REFERENCES
[1] Sunil Thomas, K.Nithiyananthan, “A Novel method to implement MPPT Algorithms for PV panels on a
MATLAB/SIMULINK environment,” Journal of Advanced Research in Dynamical and Control Systems, vol. 10,
no 4, pp.31-40, 2018.
[2] Gowrishankar. K., Sunil Thomas, K. Nithiyananthan, “Wireless Integrated-Sensor Network based Subsea Tunnel
Monitoring System,” Journal of Advanced Research in Dynamical and Control Systems, vol. 10, No 12,
pp.647-658, 2018.
[3] Gowrishankar.K. K.Nithiyananthan., M.Priyanka, Sunil Thomas, GSM based dual power enhanced LED display
notice board with motion detector,” International Journal of Engineering and Technology, vol. 7, pp. 559-566,
2018.
[4] Syahrel Emran Bin Siraj, Tan Yong Sing, Raman Raguraman, Pratap Nair Marimuthu, K. Nithiyananthan,
“Application of Cluster Analysis and Association Analysis Model Based Power System Fault Identification,”
European Journal of Scientific Research, vol. 138, pp. 16-28, 2016.
[5] K. Nithiyananthan, Pratap Nair, Raman Raguraman, Tan Yong Sing, Syahrel Emran Bin Siraj, “Enhanced R
package-based cluster analysis fault identification models for three phase power system network,” International
Journal of Business Intelligence and Data Mining, vol. 14, pp. 106-120, 2019.
[6] Sekath Varma, K. Nithiyananthan, “MATLAB Simulations based Identification Model for Various Points in Global
Positioning System,” International Journal of Computer Applications, vol. 138, no. 13, pp15-18, 2016.
[7] S. Samson Raja , R. Sundar, A.A mudha, K. Nithiyananthan, “Unit Commitment Calculator Model for Three phase
Power System Network,” International Journal of Power and Renewable Energy Systems, vol 3, no. 1,
pp. 37-42, 2016.
[8] K. Gowrishankar, K. Nithiyananthan, Mani Priyanka, Venkatesan, G., “Neural network based mathematical model
for feed management technique in aquaculture,” Journal of Advanced Research in Dynamical and Control Systems,
vol. 18, no.1, pp. 1142-1161, 2017.
[9] Sing, Tan Yong and Bin Siraj, Syahrel Emran and Raguraman, Raman and Marimuthu, Pratap Nair and
Nithiyananthan, K., “Cosine similarity cluster analysis model based effective power systems fault identification,”
International Journal of Advanced and Applied Sciences, vol 4, no1, pp. 123-129, 2017.
[10] Pratap Nair., Nithiyananthan, K., Dhinakar, P., “Design and development of variable frequency ultrasonic pest
repeller,” Journal of Advanced Research in Dynamical and Control Systems, vol. 9, no. 12, pp. 22-34, 2017.
[11] K.Nithiyananthan, Sundar, R., Ranganathan, T., Samson Raja, S., “Virtual instrumentation based simple economic
load dispatch estimator model for 3-phase power system network,” Journal of Advanced Research in Dynamical
and Control Systems, vol. 9, no. 11, pp. 9-15, 2017.
[12] K.Nithiyananthan, T.Samson Raja, Sundar, R., Amudha, A., “Virtual stability estimator model for three phase
power system network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 4, no. 3,
pp. 520-525, 2016.
[13] Pratap Nair, M., K.Nithiyananthan, “An effective cable sizing procedure model for industries and commercial
buildings,” International Journal of Electrical and Computer Engineering, vol. 6, no. 1, pp. 34-39, 2016.
[14] Pratap Nair, M., K.Nithiyananthan, “Feasibility analysis model for mini hydropower plant in Tioman Island,
Malaysia,” Distributed Generation and Alternative Energy Journal, vol. 31, no. 2, pp. 36-54, 2016.
[15] K.Nithiyananthan, Umasankar, “Environment friendly voltage up-gradation model for distribution power systems,”
International Journal of Electrical and Computer Engineering, vol. 6, no. 6, pp. 2516-2525., 2016.
[16] Sureshkumaar, G., Kannan, N., Thomas, S., Karthikeyan, S.P., “Matlab/simulink simulations based modified sepic
DC to DC converter for renewable energy applications,” Journal of Advanced Research in Dynamical and Control
Systems, vol. 11, no. 4, pp. 285-295, 2019.
[17] G. Venkatesan, K. Nithiyananthan, “Static and Transient Thermal Analysis of Power Factor Converters in Switched
Reluctance Motor Drive,” Journal of Advanced Research in Dynamical & Control Systems, vol 10, no 13,
pp.1656-1662, 2018.
[18] K.Nithiyananthan, Ramachandran, V., “Effective data compression model for on-line power system,” International
Journal for Engineering Modelling, vol. 27, no. 3-4, pp. 101-109, 2014.
[19] K.Nithiyananthan, Ramachandran, V., “Distributed mobile agent model for multi area power systems automated
online state estimation,” International Journal of Computer Aided Engineering and Technology, vol. 5, no. 4,
pp. 300-310, 2013.
[20] K.Nithiyananthan, Ramachandran, V., “Versioning-based service-oriented model for multi-area power system
online economic load dispatch,” Computers and Electrical Engineering, vol. 39, no. 2, pp. 433-440, 2013.
[21] K.Nithiyananthan, Ramachandran, V., “Location independent distributed model for on-line load flow monitoring
for multi-area power systems,” International Journal for Engineering Modelling, vol. 24, no. 1-4, pp. 21-27, 2011.
[22] K.Nithiyananthan, Loomba, A.K., “MATLAB/Simulink based speed control model for converter controlled DC
drives, International Journal for Engineering Modelling, no. 24 no. 1-4, pp. 49-56, 2011.
[23] Thomas, S., K. Nithiyananthan., Eski, A., “Investigations on transient behavior of an energy conservation chopper
fed DC series motor subjected to a change in duty cycle,” Journal of Green Engineering, vol. 9, no. 1, pp. 92-111.
2019.
[24] K.Nithiyananthan, Ramachandran, V., “A plug and play model for JINI based on-line relay control for power
system protection,” International Journal for Engineering Modelling, vol. 21, no. 1-4, pp. 65-68, 2008.
[25] K.Nithiyananthan, Ramachandran, V., “A distributed model for capacitance requirements for self-excited induction
generators,” International Journal of Automation and Control, vol. 2, no. 4, pp. 519-525, 2008.

3575
[26] Nithiyananthan. K, Ramachandran.V, “Component Model Simulations for Multi-Area Power System Model for
On-Line Economic Load Dispatch,” International Journal for Emerging Electric Power Systems, vol 1,
no 2, 2004.
[27] K. Nithiyananthan., Elavenil. V., “CYMGRD Based Effective Earthling Design Model for Substation,”
International Journal for Computer Applications in Engineering Sciences Asia, vol. 1, no 3, pp. 341-346, 2011.
[28] K.Nithiyananthan, Ramachandran, V., “Distributed mobile agent model for multi-area power system on-line
economic load dispatch,” International Journal for Engineering Modelling, vol. 17, no. 3-4, pp. 87-90, 2004.
[29] Youssef A. Mobarak, A.M. Hemeida, Nithiyananthan Kannan, Voltage and Frequency based Load Dependent
Analysis Model for Egyptian Power System Network, Journal of Advanced Research in Dynamical & Control
Systems, USA. Vol 11, No 6, 2019 Pp.971-978.
[30] Low Kak Hau, Gowrishankar, Nithiyananthan. K, “Development Of Prototype Model For Wireless Based Control
Pick And Place Robotic Vehicle,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 14,
no. 1, pp 110-115, April 2015.
[31] K.Nithiyananthan, Ramachandran, V., “A distributed model for multi-area power systems on-line dynamic security
analysis,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 3, pp. 1765-1767, 2002.
[32] K.Nithiyananthan, Ramachandran, V., “EJB based component model for distributed load flow monitoring of multi-
area power systems,” International Journal for Engineering Modelling, vol. 15, no. 1-4, pp. 63-67, 2002.
[33] Sureshkumaar, G., K.Nithiyananthan., Thomas, S, “MATLAB/SIMULINK based simulations of KY converter for
PV panels powered led lighting system,” International Journal of Power Electronics and Drive Systems, vol. 10,
no. 4, pp. 1885-1893, 2019.
[34] VimalRaj, S., Suresh Kumar, G., Thomas, S., K.Nithiyananthan, “MATLAB/SIMULINK based simulations on
state of charge on battery for electrical vehicles,” Journal of Green Engineering, vol. 9, no. 2, pp. 255-269, 2019.
[35] Mobarak, Y.A., K.Nithiyananthan, “Vertically integrated utility power system structures for egyptian scenario and
electricity act,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 7,
pp. 766-772. 2019.
[36] A. Camargo, et al., “An image processing based algorithm to automatically identify plant disease visual
symptoms,” Biosystems Engineering, vol. 102, no. 1, pp. 9-21, 2009.
[37] D. Sabine Bauer, et al., “The Potential of Automatic Methods of classification to identify Leaf diseases from
Multispectral images”, Precision Agric, vol 12, pp.361-377, 2011.
[38] D. Al-Bashish, et al., “Detection and classification of leaf diseases using K-means-based segmentation and neural
networks based classification,” Inform. Technol. J., vol. 10, pp. 267-275, 2011.
[39] A. Clément, et al., “A New Colour Vision System to Quantify Automatically Foliar Discolouration Caused by
Insect Pests Feeding on Leaf Cells,” Biosystems Engineering, vol. 133, pp. 128–140, 2015. ISSN 1537-5110,
doi: 10.1016/j.biosystemseng.2015.03.007
[40] R. Zhou, et al., "Disease Detection of Cercospora Leaf Spot in Sugar Beet by Robust Template Matching",
Computers and Electronics in Agriculture, vol. 108, pp. 58-70, 2014.
[41] J.G.A Barbedo, et al., “A New Automatic Method for Disease Symptom Segmentation in Digital Photographs of
Plant Leaves,” European Journal of Plant Pathology, vol. 147, no. 2, pp. 349–364, 2016.
[42] D. P Hughes, et al.,"An Open Access Repository of Images on Plant Health to Enable the Development of Mobile
Disease Diagnostics,” Cornell Universty 2015.
[43] A. Borji, et al., “Salient Object Detection: A Benchmark,” IEEE Trans. Image Process., vol. 24, no. 12,
pp. 5706-5722, Dec. 2015.
[44] Bardsley, et al., “Reliability and accuracy of visual methods to quantify severity of foliar bacterial spot symptom on
peach and nectarine,” Plant Pathology, vol. 62, no. 2, 2012.
[45] Zhang, et al., "Orthogonal Locally Discriminant Projection for Classification of Plant Leaf Diseases",
Ninth International Conference on Computational Intelligence and Security, pp. 241- 245, 2013.
[46] Pilli, et al., "eAGROBOT-A robot for early crop disease detection using image processing,” International
Conference on Electronics and Communication Systems (ICECS), pp. 1-6, 2014.
[47] Nithiyananthan.K, Mobarak, Y., Alharbi, F.Application of cloud computing for economic load dispatch and unit
commitment computations of the power system network,Advances in Intelligent Systems and Computing, 1108
AISC,2020 pp. 1179-1189.

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