# Unified View of Damage leaves Planimetry & Analysis Using Digital Images
Processing Techniques

Pijush Kanti Kumar  Department of Information Technology
Government College of Engineering & Textile Technology
Serampore, Calcutta, India
<EMAIL_ADDRESS>DeepKiran Munjal  Sunita Rani  Department of Computer
Applications
G. L. Bajaj Institute of Technology and Management
Greater Noida, Uttar Pradesh, India
<EMAIL_ADDRESS>Department of Engineering Administration
Noida Institute of Engineering and Technology
Greater Noida, Uttar Pradesh, India
<EMAIL_ADDRESS>Anurag Dutta  Liton Chandra Voumik  Department of
Computer Science & Engineering
Government College of Engineering & Textile Technology
Serampore, Calcutta, India
<EMAIL_ADDRESS>Department of Economics
Noakhali Science and Technology University
Noakhali, Bangladesh
<EMAIL_ADDRESS>A. Ramamoorthy  Department of Mathematics
Velammal Engineering College, Anna University
Chennai, Tamil Nadu, India
<EMAIL_ADDRESS>
###### Abstract

The detection of leaf diseases in plants generally involves visual observation
of patterns appearing on the leaf surface. However, there are many diseases
that are distinguished based on very subtle changes in these visually
observable patterns. This paper attempts to identify plant leaf diseases using
image processing techniques. The focus of this study is on the detection of
citrus leaf canker disease. Canker is a bacterial infection of leaves.
Symptoms of citrus cankers include brown spots on the leaves, often with a
watery or oily appearance. The spots (called lesions in botany) are usually
yellow. It is surrounded by a halo of the leaves and is found on both the top
and bottom of the leaf. This paper describes various methods that have been
used to detect citrus leaf canker disease. The methods used are histogram
comparison and k-means clustering. Using these methods, citrus canker
development was detected based on histograms generated based on leaf patterns.
The results thus obtained can be used, after consultation with experts in the
field of agriculture, to identify suitable treatments for the processes used.

###### Index Terms:

Digital Image Processing, k – Means Clustering, Citrus Leaf Canker Disease

##  I Introduction

In today’s world, the farmland volume serves as an additional source of food.
Agriculture’s productivity has a significant impact on the Indian economy. As
a result, it is crucial to identify plant diseases in the field of
agriculture. Use of an automatic disease recognition system is advantageous
for spotting a plant pathogens in its very early stages. For example in the
case, the United States has pine trees that are susceptible to a dangerous
disease called little leaf disorder. The affected tree grows slowly and
perishes within six years. Parts of the Southern US, including Alabama and
Georgia, are affected by it. Early diagnosis in these situations might have
been beneficial. The only method currently in use for identifying and
detecting phytopathogens is professional assessment using only one’s unaided
eye. This requires a sizable group of specialists and ongoing vegetation
surveillance, both of which are very expensive when dealing with large
farmlands. Meanwhile, in some nations, farmers lack access to the necessary
resources and even the knowledge to speak with experts. Because of this,
consulting experts is expensive and time-consuming. The recommended method
works well in these circumstances for keeping an eye on huge expanses of
harvests. Visual diagnosis of plant diseases is more time-consuming, less
accurate, and only practicable in a few locations. However, using an automatic
detection method will require less work, less time, and result in a higher
degree of accuracy. Brown and yellow spots, early and late scorch, and certain
other common bacterial, viral, and fungal diseases are all seen in plants. The
size of the diseased area is measured using image processing, and the
difference in color of the damaged area is also determined. The method used
for breaking down or classifying an image into numerous components is known as
image segmentation. Image segmentation can be done in a variety of cases right
now, from the straightforward segmentation process to sophisticated color
segmentation methods. Usually, these product attributes to elements that
people can easily differentiate into separate elements and see. There are
numerous techniques for segmenting images because computers lack the ability
to recognize objects intelligently. The image’s unique components are the
basis for the segmentation algorithm. This could be a section of an image,
color features, or boundary details. Diseased leaf spots play an important
role in plant growth environment. Diseases can also be easily identified with
the help of affected areas in culture [1]. Usually, of course, leaves clearly
show infected areas and are easily identifiable. Therefore, we can say that
plant colour change is an essential aspect of notification. If the crop is in
good health, the crop will have different colours, but if the crop dies from
some harmful pathogen, it will automatically change colour. Plant diseases
affect specific parts. This can lead to decreased productivity. The main
method used in practice to detect plant diseases is early observation by a
specialist, exposing the eye. In this feature retrieval-based investigation,
influenced leaf parts were examined using the suggested diagonal disagreement
of border, color, and appearance variability features. K-Means clustering was
employed. Six different disease types were predicted using this method.
Results of the performance assessments were eventually achieved. This reviewed
work [2] is based on methods to reduce computational complexity through
improved automated crop disease detection. This is due to the fact that it
significantly harms the agricultural industry and makes the disease’s symptoms
visible. Once more, the possibility for treating and preventing infected
plants is clear. In order to detect and categorize plant diseases, it is
necessary to look for reliable, affordable, and accurate techniques. Test
photographs of various cotton sheets were initially implemented. The pictures
that are are then captured using image processing approaches, and helpful
features are extracted for additional analysis. uses a variety of statistical
techniques to categorize the images in accordance with the precise issue of
the influenced leaf spot regions. The analysis of the best corresponded to of
characteristic conclusions for leaves impacted by the perfect solution was
done using the selection of features. In the classification phase [3], feature
values corresponding to the variance of edge, colour and texture features are
stored in the image domain. Based on the affected areas of leaf diseases, the
affected sites were identified.

##  II Materials and Methods

###  II-A Materials

The following materials were taken into use.

  1. 1.

Nikon Make 12.5 Megapixels Digital Camera

  2. 2.

Personal Computer

  3. 3.

White A4 Paper Sheet for background

  4. 4.

MATLAB 2013 version or above

  5. 5.

Photographs of the leaves were arranged in number.

###  II-B Methods

####  II-B1 Graphical Method

A leaf with a measurement region was positioned on graph paper with a 1 mm-
wide grid. On graph paper, the sheets are meticulously and precisely defined
with the aid of a pencil. It was determined how many grids the sheet’s detail
encompassed in total. The boundary detail is regarded as 1 if it takes up over
fifty percent of the grid; otherwise, it is addressed as 0. The precise leaf
area is represented by an array of grid numerals.

####  II-B2 Image Processing Method

A wider audience are capable of determining leaf area thanks to the MATLAB-
based technique used for the processing of images, which is a partially
automated technique. Code is created using MATLAB 2013 or later. For the
latest releases of the application, this code is functional. The procedure is
as follows:

  1. 1.

Study the picture.

  2. 2.

RGB to grayscale conversion.

  3. 3.

Create a binary picture from the image that is grayscale.

  4. 4.

Do the leaf area calculation.

##  III k Means Clustering

Data can be grouped in a variety of ways, however the most popular method is
the k-Means algorithm [4], which aims to make categories become more
comparable while simultaneously maintaining them as far apart as feasible. In
essence, distance calculations using Euclidean distance are performed using
k-Means. Euclidean distance calculates the distance between two given points
$(x_{1},y_{1})$ and $(x_{2},y_{2})$ using the following formula

$Distance_{euclidian}=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}$ (1)

The above formula captures distance in 2D space, but the same holds true in
multidimensional space as the number of terms added increases. The ’k’ in
k-Means represents the number of distinct clusters into which the data is
divided. A fundamental caveat [5] of the k-Means algorithm is that the data
must be continuous in nature. It will not work if your data is categorical in
nature.

###  III-A Data Preparation

As was previously addressed, the majority of methodologies for clustering,
including k-Means, rely on the concept of separation. They compute their
distance from a specific point and make an effort to reduce it. When distinct
variables utilize distinct components, something occurs. For instance, I want
to divide the general population of India into different groups since weight
is measured in kilograms of weight while heights is measured in centimeters.
As can be observed, the variable’s components have a significant impact on the
matrix of distances above. As a result, standardizing the information prior to
seeking clustering activities is an excellent decision.

###  III-B Algorithm

k-Means is an iterative clustering process. This is repeated until an optimal
solution or cluster is reached in the problem space. The following pseudo-
example covers the basic steps involved in k-means clustering, which is
commonly used to cluster data.

Start by deciding how many clusters you want, in the present instance three.
The k-Means algorithm attempts to connect the closest points to the
arbitrarily data centers it starts with.

Figure 1: Step by Step application of the k Means Clustering

###  III-C Image acquisition

The leaf measuring the affected area is placed on a black background with no
light reflection [6]. Hold the camera horizontally to the paper. The shooting
distance is neither too close nor too far. The photo has been adjusted to
cover only the background. See Fig. 2.

Figure 2: Original Image

###  III-D Observing the picture

The image is saved in the computer as $\mathtt{b3.jpeg}$. This image is read
for further processing by MATLAB.

###  III-E Dividing the Image

The initial picture is divided into two distinct kinds of images in this phase
based on how its colors vary. These pictures were chosen for their ability to
show impacted and untouched leaf regions. This is done by the k-Means
clustering method. The algorithm for this method is shown below.

  1. 1.

Read the image.

  2. 2.

Change an image’s color space from RGB towards LAB.

  3. 3.

Use k-means clustering to categorize the colors in the ’a*b*’ space.

  4. 4.

With the help of the k-means outcomes identify each pixel in the picture.

  5. 5.

Make pictures that divide the initial picture into sections based on color.

After applying the above algorithm segmented images were obtained. See Fig. 3
and Fig. 4.

Figure 3: Cluster I

Figure 4: Cluster II

###  III-F Convert the image (Cluster – I) into Binary image

After reading the image, I need to first convert this image from RGB to
grayscale [7] and then convert the grayscale image to binary. In this image,
the white – coloured areas indicate the unaffected parts of the leaves, as
shown in the image in Fig. 5. Useful for calculating the total number of
pixels in unaffected areas of the sheet.

Figure 5: Binary Version of Cluster I

###  III-G Convert the image (Cluster – II) into Binary image

After converting the image into binary image, image in Fig. 6 is obtained
where white coloured regions indicate affected regions of the leaf. It helps
to calculate total number of pixels of affected regions [8] of the leaf.

Figure 6: Binary Version of Cluster II

###  III-H Pixel Calculation

####  III-H1 From Cluster I

WP (Pixels of unaffected regions) = 195612
where, WP denotes white pixels of Cluster - I.

####  III-H2 From Cluster II

WP1 (Pixels of affected regions) = 41246
where, WP1 denotes white pixels of Cluster - II.

Now, total pixels of the leaf area that is obtained is shown below.

$TP=WP+WP1$ (2) $TP=195612+41246=236858$

Percentage of affected pixels can be obtained by the following as

$Error=\frac{WP1\ (pixels\ of\ affected\ regions)}{TP\ (Total\ pixels\ of\
the\ leaf\ area)}\times 100$

ERROR (%) = 17.4138 %

##  IV Results and Discussion

Leaves were selected from different plots and different types of citrus leaves
to test the performance [9] of the new measurement system. Leaf area is
calculated using the square grid method and is considered standard area. When
we obtained sampled data from different affected leaves. Then you can get a
clear idea about the whole damaged sheet in relation to the specific area.
This proposed method is faster [10] and more accurate than any standard
method.

##  V Conclusion

This article discusses a method for measuring the area of citrus leaves using
the processing of digital images. Calculating the quantity of leaves that are
damaged has been demonstrated to be possible with the executed algorithmic
structure. Although more time-consuming, grid calculating strategies are
highly precise for measuring the surface area of leaves. The image processing
method is also a fast method [11 - 21] with high accuracy [22] and precision
[23]. You may modify the leaf picture at any moment if you solely preserve it
[24]. This can serve as an empirical basis for creating an affordable leaf
area meter that meets precision farming requirements. For precise area
computations and disease incidence projections for chemical pesticides and
application of fertilizer, the proportion of error aspect is crucial and
required.

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