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International Journal of Engineering, Management & Sciences (IJEMS)
ISSN-2348 –3733, Volume-3, Issue-8, August 2016
An Overview on Detection and Classification of
Plant Diseases using Image Processing
Neha Sharma, Aishwary Kulshrestha, Himanshu bhojwani
Abstract— It is being great difficult for farmer to changing
As we know management of plant requires close monitoring
especially for the management of disease that can affect
production significantly and subsequently the postharvest
life. The naked eye observation of experts was the main
approach adopted in practice for detection of plant diseases
which is long time consuming process and less efficient and
prohibitively expensive in large farms. The machine vision
system now a day is normally consists of computer, digital
camera and application software Automatic detection of
plant leaf diseases is an essential research topic as it may
prove benefits in monitoring large fields of crops and thus
automatically detect the symptoms of diseases as soon as
they appear on plant leaves. Therefore looking for fast,
automatic, less expensive and accurate method to detect
disease by calculating leaf area
from one disease control policy to another. Depending upon
only pure naked-eye observation to detect and classify
diseases can be expensive. Various plant diseases pose a great
threat to the agricultural sector by reducing the life of the
plants. Plant diseases have put us into a dilemma as it can
cause significant reduction in both quality and quantity of
agricultural products. Automatic detection of plant diseases is
an essential research topic as it may prove benefits in
monitoring large fields of crops the present work is aimed to
develop a simple disease detection system for four type of plant
disease. The
Proposed system is a complete software solution for automatic
detection and classification of plant leaf diseases and their
medication process based on environmental condition
This research work focuses on conditionally look over a
complete software based method for evaluating plant diseases
using image processing techniques. Analysis of the plant
characteristics or the unusual appearance of plant leaves
referred the observable disorder in plant leaves i.e. Plant leaves
is infected by some disease. Presently crops or we can say
plants showing very unusual characteristics. Therefore,
detection of such diseases is very necessary at the early stages.
The work begins with fetch diseased leaf images. Color feature
like LAB features are extracted from the result of
segmentation for segmentation purpose we use k means
clustering and artificial neural network (ANN) is then trained
by choosing the feature values that could identified the type of
diseased leaf appropriately. Experimental results showed that
classification performance by ANN taking feature set is better
with an accuracy of 90%. The present work proposes a
methodology for detecting cotton leaf diseases early and
accurately, using diverse image processing techniques and
artificial neural network (ANN).
We will start by taking an input image of defected plant
leaves and will extract the features of leaves. With the help
of this feature we will compare our defected plant leaves
with the trained database present there. We will use
Artificial Neural Network as our classifier for comparison of
disease plant leaf. An artificial neural network (ANN),
usually called neural network (NN), is a mathematical
model or computational model that is inspired by the
structure and/or functional aspects of biological neural
networks. A neural network consists of an interconnected
group of artificial neurons, and it processes information
using a connectionist approach to computation. In most
cases an ANN is an adaptive system that changes its
structure based on external or internal information that flows
through the network during the learning phase apart from
this we have use Anfis for to determine the severity of
present disease in a particular plant leaf so according to its
grading level we will treat them
We have created a database of diseased plant leaf
considering four type diseases they are Botrytis Blight, Leaf
Spot, Powdery mildew and Rust then features are extracted
and database is created and comparison is done with the
features which are extracted from the input test image.
.
Index Terms— MATLAB, Artificial Neural Network(ANN),
Anfis, Plant leaf Disease
I. INTRODUCTION
In our project we have implemented a complete system
which will detect and classify 4 types of plant leaves
diseases using Artificial Neural Network Technology and
will do its grading for severity management and after that
will provide medication process details.
II. PROPOSED SYSTEM
We proposed a system which is a complete software
solution for detecting and classifying the diseases of plant
leaves which will help the farmers to detect disease and take
proper prevention to enhance the production of agriculture
.We took the pictures of diseased plant leaves and
performed various preprocessing techniques on them so that
we can get the proper area for further processing can be
obtained then for severity management we use Anfis model
Manuscript received August 25, 2016.
Neha Sharma, M-tech Scholar Department of CSE – Rajasthan
Technical University RCEW Jaipur, India
Aishwary Kulshrestha, M-Tech Coordinator Department of CSE –
RCEW Jaipur, India
Himanshu Bhojwani, Technical Head- Contrivance IT Solutions Pvt. Ltd
9
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An Overview on Detection and Classification of Plant Diseases using Image Processing
in our project which will do grading of disease and will tell
us that how severe the disease is . We used ANN as the
classifier for testing the input test image with the database
image so that proper disease can be detected. The main
objective of the proposed work is to automatic detection and
classification of diseases and provides proper medication
process on the basis of environmental condition. Detection
of plant leaf diseases can be done early and accurately using
artificial neural network.
III. SYSTEM ARCHITECTURE
There are basic seven steps that we are going to use for
leaves disease detection and classification. Firstly input
image will be preprocessed and clustering will be done to
get the required area where disease is present after that
required prominent features are being extracted. With the
help of these features input image will be compared using
ANN and disease will be detected.
It consists of following seven blocks. We started our project
with the training of ANN for different 4 types of diseased
plant leaves. Then we give features of input test image to
ANN which compares that features with the features of
database image and give us proper output.
leaves and according to the features that are extracted from
those images training of the ANN is done.
Input Image: In this image any four disease of plant leaves
in or outside of the database can be tested by the ANN that
we have trained here we will take the leaves of particular
required four plants leaves
Image Pre-processing: Image pre-processing is the name
for operations on images at the lowest level of abstraction
whose aim is an improvement of the image data that
suppress undesired distortions or enhances some image
features important for further processing and analysis it task
does not increase image information content. In this step
image is pre-processed to improve the image data that
suppress undesired distortions, enhances some image
features important for further processing and analysis task. It
includes color space conversion, image enhancement, and
image segmentation. The RGB images of leaves are
converted into LAB color space representation. The purpose
of the color space is to facilitate the specification of colors
in some standard accepted way. RGB images converted into
LAB color space representation. Because RGB is for color
generation and LAB for color descriptor and LAB model is
an ideal tool for color perception in further clustering
Segmentation & Feature Extraction: Image segmentation
is used to determine components of an image into which are
more significant and easier to examine. Image segmentation
is done using k-means clustering algorithm which is easy to
analyze images Clustering needs various image objects
which are easily separable from each other to form number
of clusters.
Figure 3.1: The basic procedure of the proposed
approach
Creating Database: We started our project by creating
database used for training and testing by ANN. Database
contains all the images of the 4 type of diseases of plant
leaves that would be used for training and testing. The
image database consists of image samples. For each of the
disease we have taken some images of the diseased plant
10
K Means clustering
Clustering is the sorting of items into various groups so that
the data in every subset show some common part according
to some defined distance measure. An image can be grouped
using shapes, textures or any other information that can be
taken from the image itself. In K-means, data parts are
divided into predefined number of clusters. Firstly the
centroids of defined clusters are set randomly. The next step
is to take every point belonging to a given data set and link
it to the closest centroid. Each pixel is assigned to the cluster
based on the closeness of the pixel, which is determined by
the Euclidian distance measure. r*g*b color image into
L*a*b space. Now on the output of clustering data feature
are being extracted which contain maximum information
which will be used further here system will predict the
cluster name or figure where disease is present and will
process it to next step
Then feature extraction will be done The aim of feature
extraction phase is to find and extract features that can be
used to determine the meaning of a given sample. Now we
will extract the feature of clustred which is predicted by
system and contains maximum information. Grey Level Cooccurrence Matrices (GLCM) is a statistical method which
is used here for feature extraction or texture classification. It
has been an important feature extraction method in the
domain of texture classification that computes the
relationship between pixel pairs in the image. The textural
features can be calculated from the generated GLCMs, e.g.
contrast, correlation, energy, entropy and homogeneity
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International Journal of Engineering, Management & Sciences (IJEMS)
ISSN-2348 –3733, Volume-3, Issue-8, August 2016
Grading and severity determination using Anfis: We
have used Anfis in this project for the purpose of grading the
plant leaf disease and find the severity of that particular
disease. Grading define the spreading of a particular disease
in a plant leaf and so then we can identify the severity of
that particular disease in a plant leaf So that we can
understand severity of a particular disease and cure that
disease timely and according to the level of its
dangerousness we can provide the dose of medicines too
Classifier as ANN: Classifier will compare the input image
with features of diseased leaves present in database. We are
using ANN as our classifier.
Weather Based API use through Internet: once the
disease detection and classification is done using ANN we
have used a weather based API through internet and get the
weather parameter on the basis of these weather parameter
we will provide the medication process further
can be possible at early stage. Neural Network efficiency is
used to classify appropriately the plant disease apart from
that Anfis is used to determine the severity of present
disease. Along with that K-Means clustering gives us a
confidence on the obtained results and finally required
medication processing to curing that diseased plant is taken
into account to make it healthy again according to its
Severity level so in short we can say that after the input
image to the system is provided then system will
automatically process all the steps and will provide the final
results We have given the comparison of our work done
with earlier work done in below table 4.1 and disease
detection and classification in table 4.2
Table 4.2: Testing Result for disease detection and
classification
Table 4.1: Technology implemented work comparison
with earlier work done
V. CONCLUSION
1.
2.
3.
4.
5.
IV. RESULT
This is one of the fast and complete automatic methods for
timely detection and classification and curing of diseases
11
6.
An fully automatic software solution for Plant
diseases detection and classification at early stage
by using Image processing techniques is
implemented successfully
System performance is tested and found
satisfactory in terms of accuracy and efficiency for
both real time as well as database images
We have done K-means clustering after that Anfis
is used to determine severity
We determine the severity of present disease and
do its grading on the basis of that we will do further
medication process accordingly
This system has Accuracy of 90% which made the
system most accurate and precise method of
identification and classification of plant diseases
We use ANN for classification of present disease in
plant leaf
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An Overview on Detection and Classification of Plant Diseases using Image Processing
7.
8.
This system also have feature to process real time
images. So, monitoring of distant images can be
possible.
We have done medication process on the basis of
weather based parameter using Internet
.
ACKNOWLEDGMENT
I am very Thankful and would like to express my sincere
gratitude to my advisor Aishwary Kulshrestha for the
continuous
support
of
my
M-Tech dissertation and related research, for his patience,
motivation, and immense knowledge. His guidance helped
me in all the time of research and writing of this thesis. I
could not have imagined having a better advisor and mentor
for my M-Tech dissertation.
Engineering Volume 3, Issue 11, November 2013 Image Processing
Techniques for Detection of Leaf Disease
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regression for apple disease detection‖, Measurement, Sept-2014,
(Elsevier)
[14]. Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz,Ali Yeon Bin
Md Shakaff,Rohani Binti S Mohamed Farook. Feasibility Study on
Plant Chili Disease Detection Using Image Processing Techniques.
IEEE, 2012
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