International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
Plant Leaf Recognition using Neural network classifiers
Selvakumari M1, Mrs D. Manohari2
PG Student, St. Joseph’s College of Engineering, Chennai
2
Associate Professor, Department of MCA, St. Joseph’s College of Engineering, Chennai
[email protected]
1

Abstract-Shape modeling techniques involve identification of
leaf structures and classifying them according to their
structure. Moment invariant model deals with rotation,
translation and scaling of images for the purpose of
identification. Centroid radii model focuses on identifying the
shape based on coordinates. This project proposes these two
techniques to find out the scientific name and disease
classification of leaves which can lead to the leaf being
further researched. A neural network (NN) is designed to
implement this cause further enhancing the process of
classification.This classification can be used in the field of
medicine to identify the rare medicinal species of plants. Used
by the forest department to classify the plant species and to
save the plant species which are at the brink of extinction.A
Neural Network is represented by a set of nodes and arrows.
Where a node corresponds to a neuron and an arrow
corresponds to a connection. An Artificial Neural Network
(ANN), often just called a Neural Network (NN), is a
mathematical model or computational model based on
biological neural networks. It consists of an interconnected
group of artificial neuron and processes information. It is
classification of normal and abnormal leaf. It is used for
medical fields.
Keyword: Artificial neural network, Support Vector Machine,
Fuzzy logic, Principal components analysis, probabilistic
neural network,linear decrement analysis,Independed
components analysis.
I. INTRODUCTION
hape modeling techniques is classify the plant species. India
is an agricultural country and about seventy percentage of the
population depends on agriculture, where plant leaf diseases
widely affect the production of the country. Here this survey
provides a brief description on various identification techniques.
Disease identification is a tedious task and mostly diseases are
seen on the leaves or stems of the plant.
65% population depends on agriculture. The crop losses
due to diseases are approximately 10 to 30%. Farmers judge the
diseases by their experience but this is not accurate and in a
proper way. Sometimes farmers call the experts for detecting the
diseases but it is time consuming way. The diseases are mostly
on leaves and on stem of plant. The diseases are viral, bacterial,
fungal,diseases due to insects,rust, nematodes etc. on plant. It is
important task for farmers to find out these diseases as early as
possible.
The Image RGB characteristic pixel numbering strategies is
S
broadly connected to farming science, and it has extraordinary
point of view particularly in the plant insurance field, which at
last prompts crop administration.
The purposes for applying image analysis in plants are as
follows:
 To detect the boundaries of the affected area.
 To identify the Object correctly
 To find diseased leaf, stem, fruit.
 To quantify affected area by disease.
 To determine the color and feature of the affected area
As known, plants are very important for human beings.
The photosynthesis of plants can maintain the balance of carbon
dioxide and oxygen in the atmosphere.
At the same time, plants are important resources of food
and some products, and they also play a vital role in water
conservation, inhibiting desertification and improving climate.
However, the plant diseases cause significant reduction in both
quality and quantity of agricultural products (Ananthi and
Varthini, 2012; Wang et al., 2008; Camargo and Smith, 2009;
Arivazhagan et al., 2013). In 1943, in north eastern India, it is
estimated that the outbreak of the rice helminthosporiose caused
a heavy loss of food grains and death of a million people
(Ananthi and Varthini, 2012).
In 2007, in Georgia (USA), it is estimated that the plant
disease losses was about $539.74 million, about $185 million
was spent to control the diseases, and the rest was the value of
damage caused by the diseases. So plant disease resistance and
management are crucial to the reliable production of food. In
fact, about 80% to 90% of disease on the plant is appeared on its
leaves. So we interest in the plant leaf rather than whole plant.
There are many leaf based plant disease recognition
methods (Sabine Bauer et al., 2011; Al-Bashish et al., 2011; AlHiary et al., 2011; Dheebet al. 2010; Arivazhagan et al ., 2013).
But, there is not an effective method because of the complexity
of color and shape of the disease leaves. In this paper, a disease
recognition method is proposed and the major steps of plant
disease identification are introduced.
A.Identification of plant leaf
Leaf shape is a primary tool in plant identification. Descriptions
often go into minute detail about general leaf shape, and the shape
of the leaf apex and base. .
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International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
In contrast to contour-based methods, region-based shape
recognition techniques have been used in [10] for leaf image
classification.
A. Plant leaf structure
For plant identification purposes, the shape of the leaf
margin, leaf tip and leaf base are key features to note. A leaf
begins at the lateral or auxiliary budLeaf blade Flattened part of
the leaf Petiole Leaf stalk Stipules Leaf-like appendages at the
base of the leaf.
The various plant leaf images are collected directly from the
agricultural field using digital camera. The white background is
set to take the flash of each leaf images for better result. In this
two different agricultural plant leaves are considered. (i.e.) Bean
leaf and Bitter gourd leaf.
Fig 1.Plant leaf identification
II. RELATED WORK
Plants play an important role in our environment .The leaf is
represented by local descriptors associated with margin sample
points. Within this local description, four multiscale triangle
representations: namely the well-known triangle area
representation (TAR), the triangle side lengths representation
(TSL) and two new representations that we denote triangle
Fig
oriented angles (TOA) and triangle side lengths and angle
2.Plant
leaf
structure
representation (TSLA).
Many methodologies have been proposed to analyze plant leaves
Agriculture is the mother of all nations. Research in
in an automated fashion. A large percentage of such works utilize
shape recognition techniques to model and represent the contour agriculture domain is aimed towards increase the quality and
shapes of leaves, however additionally, color and texture of quantity of the product at less expenditure with more profit. The
leaves have also been taken into consideration to improve quality of the agricultural product may be degraded due to plant
recognition accuracies. One of the earliest works [1] employs diseases. These diseases are caused by pathogens viz.., fungi,
geometrical parameters like area, perimeter, maximum length, bacteria and viruses. Therefore, to detect and classify the plant
maximum width, elongation to differentiate between four types disease in early stage is a significant task. Farmers require
of rice grains, with accuracies around 95%. Use of statistical constant monitoring of experts which might be prohibitively
discriminant analysis along with color based clustering and expensive and time consuming. Depending on the applications,
neural networks have been used in [2] for classification of a many systems have been proposed to solve or at least to reduce
flowered plant and a cactus plant. In [3] the authors use the the problems, by making use of image processing and some
Curvature Scale Space (CSS) technique and k-NN classifiers to automatic classification tools.
classify chrysanthemum leaves.
B. Disadvantages
Both color and geometrical features have been reported in [4] to
detect weeds in crop fields employing k-NN classifiers. In [5] the  Systems are extremely slow and inefficient classification of
plant species.
authors propose a hierarchical technique of representing leaf
shapes by first their polygonal approximations and then  It gives high complexity data modeling scheme.
introducing more and more local details in subsequent steps.  It gives less classification accuracy.
Fuzzy logic decision making has been utilized in [6] to detect
III. PROPOSED WORK
weeds in an agricultural field. In [7] the authors propose a twostep approach of using a shape characterization function called The proposed system implements a leaf recognition algorithm
centroid-contour distance curve and the object eccentricity for using easy-to-extract features and high efficient recognition
leaf image retrieval. The centroid-contour distance (CCD) curve algorithm. The main improvements are on feature extraction and
and eccentricity along with an angle code histogram (ACH) have a neural network classifier approach for leaf recognition. All
been used in [8] for plant recognition. The effectiveness of using features are extracted from digital leaf image. It is a simple and
fractal dimensions in describing leaf shapes has been explored in computationally efficient method for plant species recognition
[9].
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International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
using leaf image. The method consists of five major parts. First,
images of leaf are acquired with digital camera or scanners.
C. Applications
A. System architecture
Then the user selects the base point of the leaf and a few
reference points on the leafblades. Based on these points the leaf
shape is extracted from the background and a binary image is
produced. After that the leaf is aligned horizontally with its base
point on the left of the image. Then several morphological
features, such as eccentricity, area, perimeter, major axis, minor
axis, equivalent diameter, convex area and extent, are extracted.

Used in the field of medicine to identify the rare
medicinal species of plants

Used by the forest department to classify the plant
species

Used to save the plant species which are at the brink of
extinction
B. Preprocessing
Pre-processing of images commonly involves removing lowfrequency background noise, normalizing the intensity of the
individual particle’s images, removing reflections, and masking
portions of images. Image pre-processing is the technique of
enhancing data images prior to computational processing.
Fig 4.Image pre-processing
Image pre-processing typically denotes a processing step
transforming a source image into a new image which is
fundamentally similar to the source image, but differs in certain
aspects, e.g. improved contrast.
Fig 3. Overall Architecture Diagram
A unique set of features are extracted from the leaves by slicing
across the major axis and parallel to the minor axis. Then the
feature pointers are normalized by taking the ratio of the slice
lengths and leaf lengths (major axis). In the training phase, from
a given set of training images (segmented) the texture features
are extracted and used to train the system using a probabilistic
neural network classifier. In the classification phase, given a test
image, the leaf is segmented and the texture features are
extracted.
B. Advantages:

Useful for quick and efficient classification of plant
species.

Used to identify and save the rare species of plants
which are at the brink of extinction. Low complexity
data modeling schemes are used.
C. Morphological operation
Morphological techniques typically probe an image
with a small shape or template known as a structuring element.
The structuring element is positioned at all possible locations in
the image and it is compared with the corresponding
neighborhood
Dilation, in general, causes objects to dilate or grow in
size; erosion causes objects to shrink. The amount and the way
that they grow or shrink depend upon the choice of the
structuring element. Dilating or eroding without specifying the
structural element makes no more sense than trying to low pass
filter an image without specifying the filter.
• Erosion: The basic effect of the operator on a binary
image is to erode away the boundaries of regions of
foreground pixels (i.e. white pixels, typically). Thus
areas of foreground pixels shrink in size, and holes
within those areas become larger of pixels.
Morphological operations differ in how they carry out
this comparison.
328
•
Dilation: The basic effect of the operator on a binary
image is to gradually enlarge the boundaries of regions
of foreground pixels (i.e. white pixels, typically). Thus
International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
areas of foreground pixels grow in size while holes
within those regions become smaller.
D. Segmentation through ANN method
Image segmentation is a processing task that aims to
locate different objects and boundaries in the image content. Its
goal is to partition an image into multiple segments "sets of
pixels" that are more meaningful to analyse. For which, the
image is divided into two parts: background and foreground,
where the foreground is the interesting objects and the
background is the rest of the image. All the pixels in the
foreground are similar with respect to a specific characteristic,
such as intensity, color, or texture.
Image segmentation methods have been classified into
numerous approaches: The approaches are threshold Based
Image Segmentation, Region Based Image Segmentation, Edge
Based Image Segmentation and cluster Based Image
Segmentation.
E. Feature extraction
Feature extraction involves simplifying the amount of
resources required to describe a large set of data accurately. In
pattern recognition and in image processing, feature extraction is
a special form of dimensionality reduction. When the input data
to an algorithm is too large to be processed and it is suspected to
be notoriously redundant (much data, but not much information)
then the input data will be transformed into a reduced
representation set of features (also named features vector).
Transforming the input data into the set of features is called
features extraction. If the features extracted are carefully chosen
it is expected that the features set will extract the relevant
information from the input data in order to perform the desired
task using this reduced representation instead of the full size
input.
The aim of this phase is to find and extract features that
can be used to determine the meaning of a given sample. In our
project we are considering color as desired feature. We will
convert RGB image in hue saturation and value for getting
features.
trained using unsupervised learning to produce a lowdimensional
(typically
two-dimensional),
discretized
representation of the input space of the training samples, called a
map.
The Self-Organizing Map is one of the neural network models,
based on unsupervised learning (human intervention is needed
during the learning). Self-organizing maps are different from
other artificial neural networks in the sense that they use a
neighbourhood function to preserve the topological properties of
the input space.
A self-organizing map consists of components called nodes
or neurons. Each node has a weight vector of the same dimension
as the input data vectors and a position in the map space. The
nodes are usually arranged in a two-dimensional regular spacing
in a hexagonal or rectangular grid.
The self-organizing map describes a mapping from a higher
dimensional input space to a lower dimensional map space. The
procedure for placing a vector from data space onto the map is to
find the node with the smallest distance weight vector to the data
space vector.
G. Classification of disease
The International Classification of Diseases (ICD) is the standard
diagnostic tool for epidemiology, health management and clinical
purposes. This includes the analysis of the general health
situation of population groups.
It is classification of disease and finding the disease name
and to find the leaves are normal or abnormal. It is identified the
leaf shapes.
The Back propagation algorithm is supported by many
parameters such a Learning rate, Activation function and
Momentum. The training and operation of the Back Propagation
Neural Network is divided into two categories, Initial is that the
feed-forward calculation is used in bothtraining mode and testing
mode of neural network. Second, the error back propagation
calculations, is used only during
Training phase to calculate the error and to update weights
oftheneurons. These calculations are explained in the
algorithm3of the Appendix A. Fig.5 shows the configuration of
backPropagation neural network. The proposed work involves
threeLayer back propagation model where three layers are,
inputLayer, Hidden layer and output layer. Each neuron is
depictedby a circle and every interconnection, with its
associatedweight, by an arrow. The neurons labeledb are bias
neurons.
Fig 5. Multi-layered artificial neural network
F. Som pattern recognition method
A self-organizing map (SOM) or self-organising feature
map (SOFM) is a type of artificial neural network (ANN) that is
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International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
F. Fuzzy Logic
Fuzzy Logic classifiers are classification systems that
Make use of fuzzy sets or fuzzy logic which converts real-world
data values into membership degrees through the use of the
membership functions, so that these rules then can be used for
the classification process. This is done by defining “categories”
for each one of the attributes.
Fig 6.Neural network classifier
Fig7. Sample normal and abnormal leaf
Support vector machine (SVM) is a non-linear classifier, and is a
newer trend in machine learning algorithm. SVM is popularly
used in many pattern recognition problems including texture
classification. Disease name finding the method is support vector
machine. Fuzzy Logic classifiers are finding the disease.
Support Vector Machine (SVM) is a supervised
learningtechnique used for the aim of classification. SVM will
producea hyper plane that separates data points in the space; it
also increases the margin between the two data sets.
As Fuzzy logic classifier’s has very high speed they are
preferable in cases where there is limited precision in the data
values or when classification is required in real time. Fuzzy
image processing is the collection of all approaches that
understand, represent and process the images, their segments and
features as fuzzy sets.
The strength of each signal and the biases are represented
by weights and constants, which are calculated through the
training phase. After the inputs are weighted and added, the
result is then transformed by a transfer function into the output.
The transfer functions used are Sigmoid, hyperbolic tangent
functions or a step. Back propagation is a neural network
learning algorithm is used in layered feed-forward Artificial
Neural Networks. Back propagation is a form of supervised
training.
A.Artificial neural networks:
ANNs are popular machine learning algorithms that are in a wide
use in recent years. Multilayer Perception (MLP) is the basic
form of ANN that updates the weights through back propagation
during the training.
There are other variations in neural networks, which are recently,
became popular in texture classification. It is very powerful
language.
B.Principal component analysis
Principal components analysis (PCA) tries to describe the
important variability in the data in a reduced number of
dimensions. Principal component analysis (PCA) is a
mathematical procedure that uses orthogonal transformation to
convert a set of observations of possibly correlated variables into
a set of values of linearly uncorrelated variables called principal
components. The number of principal components is less than or
equal to the number of original variables.
C. Linear decrement analysis
Linear decrement analysis (LDA) is texture value extraction. It
easily understands the texture and that values, most of used for
recognition process.
D. Independed components analysis
Independed components analysis (ICA) is color value extraction.
It is used for recognized and analysis the image color, that is
normal and abnormal color and leaf structure, identified in the
leaf.
F. Support vector machine
A Support Vector Machine (SVM) is a discriminative classifier
formally defined by a separating hyper plane. In other words,
given labeled training data (supervised learning), the algorithm
outputs an optimal hyper plane which categorizes new examples.
IV. PERFORMANCE ANALYSIS
Performance Analysis is a specialist discipline involving
systematic observations to enhance performance and improve
decision making, primarily delivered through the provision of
objective statistical (Data Analysis) and visual feedback (Video
Analysis).
A performance analysis is presented for the most popular
neural network classifier, the multilayer perceptron (MLP). The
analysis is performed for a specific class of pattern recognition
problems called one-class classifier problems. The criteria used
to measure the performance are classification error,
computational complexity (measured in terms of the network
size), sensitivity to network size selection, and number of
training samples required. With regard to the network size it is
shown that networks with one hidden layer perform better than
those with two hidden layers.
Further, a lower bound on the number of nodes in the
hidden layer is derived and found to be d+1, where d is the
dimension of the data patterns. The optimal number of nodes is
shown to be somewhat larger than this (approximately 3d). In
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International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
addition the network performance is shown to be relatively
insensitive to over specification of the network size.
A. Performance of the classifiers
To evaluate the proposed leaf pant identification model, the
performance accuracy of three different classifiers; PNN, ANN
method and support vector machine (SVM) are measured. The
classification performances are measured by the Recall,
Precision and F−measure.
 FN TP TP Recall
 FP TP TP Precision
True positives (TP) refers to a correct prediction of the
classifier. False positives (FP) and False negatives (FN)
correspond to the classifier incorrect predicted. And the
F−measure is given by: For which, each digital image are
segmented by PSO-segmentation; and the HOG features vectors
are extracted from these segmented images. Then, three different
categories of classifiers; J48, naive bayes and SVM classifier are
used to measure the classification performance.
To measure the quality of the classified diseased leaf images the
performance is analysed by using four parameters, which
includes Accuracy (AC), Recall ratio, Precision and F_measure.
Fig 8.Accuracy of performance analysis
A. Accuracy
The accuracy (AC) is the proportion of the total number
of predictions that were correct. It is determined using the
equation Accuracy (AC) = (1)
𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑝 +𝑇𝑁
𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
D. F- Measure
The F-measure computes some average of the information
retrieval precision and recall metrics.
2∗𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗𝑟𝑒𝑐𝑎𝑙𝑙
𝐹𝑀𝑒𝑎𝑠𝑢𝑟𝑒 =
------------(4)
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 +𝑟𝑒𝑐𝑎𝑙𝑙
Number of correct classified bitter gourd leaves,fn is
number of misclassified bitter gourd leaves.
B. Performance evaluation measures
In FFNN classification, 58 bean leaf samples are
correctly classified and misclassified samples are 5.The correct
classification rate for beans samples 92.1%. For bitter gourd, 49
samples are correctly classified out of55.
The correct classification for bitter gourd leaves is
89.1%.The overall system classification rate for the above leaf
samples are 90.7% and the error rate of the system is
9.3%.Confusion matrix for NN.From, classification of learning
vector quantization was explained. In ANN algorithm, 60 bean
leaf samples are correctly classified out of 63 samples.
The correct classification rate for beans samples
95.1%.For bitter gourd only 7 samples are correctly classified out
of 55.The correct classification for bitter gourd leaves
is12.7%.The overall system classification rate for the above leaf
samples are 56.8.7% and the error rate of the system is43.2%.
RBF network correctly classified all diseased bean leaf
samples. But in bitter gourd only, 2l leaf samples are correctly
classified out of 55. The correct classification rate of bitter gourd
leaf samples are 38.2%. The overall system performance of RBF
classification is 71.2%.
The error rate of RBF classification for the above plant
leaf is28.8%.The performance evaluation for the above
classification techniques using Accuracy, Recall ratio, Precision
and F_measure tabulation is given below.
To evaluate the proposed leaf pant identification model,
the performance accuracy of support vector machine (SVM) are
measured.
The classification of leaves Recall true positive and
false negative performance. It is identified the leaf texture and
color recognition.
-------- (1)
B. Recall ratio
The recall or true positive rate (TP) is the proportion of
positive cases that were correctly identified,as calculated using
the equation Recall ratio = (2)
𝑇𝑃
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
----------------- (2)
𝑇𝑃+𝐹𝑃
C. Precision
Precision (P) is the proportion of the predicted positive
cases that were correct, as calculated using the equation Precision
(P) = (3)
𝑇𝑃
𝑟𝑒𝑐𝑎𝑙𝑙 =
----------------------- (3)
𝑇𝑃+𝑇𝑁
Where,
 True positives (TP) refers to a correct prediction of the
classifier.
 False positives (FP) and False negatives (FN)
correspond to the classifier incorrect predicted.
C. Experimental Results and Analysis
The classification of diseased plant leaves performance
of various neural network techniques which have been analyzed
for the 118 input leaf images. The performance evaluated for the
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International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 22 Issue 2 – MAY 2016.
neural network techniques which have been used in this paper
from the confusion matrix of their respective classifier. Shows
the confusion matrix for feed forward neural network
classification results for 118 input leaf samples.
Table 1. Classification of leaves
and SVM classifier are used to measure the classification
performance. The accuracy measures of the classifiers.
Table 2. Accuracy measures of classifiers
Methods
Accuracy
Recall
Precision
F-measure
J48
0.959
Fp
rate
0.004
ANN
0.9067
0.9365
0.8939
0.9147
NB
0.821
0.02
0.849
0.821
AVM
0.5677
0.9523
0.5555
0.7017
SVM
0.915
0.01
0.923
0.915
PNN
0.7118
1
0.6494
0.7875
The performance analysis chart reveals that the accuracy of
FFNN is higher than others and also the Precision value and
F_measure is higher than other algorithms. This indicates the
feed forward neural network classification approach is better
based on these three parameters.
Learning Vector Quantization (LVQ) is a supervised version of
vector quantization that can be usedwhen we have labeled input
data.
In this proposed method the classification techniques are used to
classify the diseased plant leaves. Here artificial neural
networking (ANN) technique is used. The ANN classification
techniques as Feed forward neural network algorithm (FFNN),
Learning vector quantizationArtificial neural networks are the very
versatiletools and have been widely used to tackle many issues.
Feed-forward neural networks (FFNN) is one of thepopular
structures among artificial neural network.the neural network
algorithm is proposed for diseased plant leaf classification. The
neural network techniques such as feed forward neural
network(FFNN), learning vector quantization (LVQ) and radial
basis function network (RBF) were tested for two different diseased
leaf image classifications such as bean and bitter
gourd leaves.
Classifier
TP Rate
Precision
F.measure
0.959
0.959
The comparison impact of applying Information Entropy
Maximization (IEM) discretization. It is clear from Table 6.2 that
identification performance of the Naive bayes and SVM
classifier is increased to 90.38% and 98.72% respectively. While,
the identification speeds for the whole three different classifiers
are improved.
Concerning to the proposed plant identification model, Figure 3
shows the F-measures of the three classifiers J48, Naive bayes
and SVM; after applying PSO segmentation only without any
pre-processing. Then, applying Information gain (IG) Feature
selection and combining IG feature selection with IEM
discritization. The time speed comparision of the three
classifiers.
V. CONCLUSION
The proposed system provides a simple and computationally
efficient method for plant species recognition using leaf image.
The computer can automatically recognize leaf by transferring
the leaf sample to the computer. This proposed system
implements a leaf recognition algorithm using easy-to-extract
features by using M-I model and C-R model techniques and high
efficient recognition algorithm with ANN classifier. Our main
improvements are on feature extraction and the classifier. All
features are extracted from digital leaf image. To add the leaves,
whose images have been clearly attained, to the system and to
provide a proper analysis of those leaves.
A. Limitation
Size of the training set is limited and time consuming.
Classification algorithms also has limitation with speed and size,
both in training and testing. There are some limitations of
extracting ambiguous color pixels from the background of the
image.
Fig 9. Neural network classification
To experiment the proposed plant identification model;
first we try to analyse the effect of PSO-segmentation before
applying IG feature selection and discritization. For which, each
digital image are segmented by PSO-segmentation; and the HOG
features vectors are extracted from these segmented images.
Then, three different categories of classifiers; J48, naive bayes
B. Future work
Future work can be developed in hybrid algorithm
such as other clustering method and NNS is order to
improve the recognition rate of final classification process.
Further needed to compute amount of disease preset on leaf. An
extension of this work will focus on developing hybrid
algorithms such as genetic algorithms and NNs in order to
increase the recognition rate of the final classification
process underscoring the advantages of hybrid algorithms.
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