The document discusses the preprocessing stages for classifying and extracting features from brinjal leaf images. The stages include: 1) Reading RGB images of leaf samples 2) Performing histogram equalization and resizing images to increase quality 3) Using k-means clustering to segment diseased portions of leaves from healthy portions for analysis 4) Converting images to HSI color space and extracting texture features using GLCM to classify images using SVM.

2. III. PREPROCESSING STAGES
• It is most common practice to have the
preprocessing of brinjal leaf images before it
has been classified and extracted. The
processing stages are
(i) Reading the images.
(ii) Image pre-processing includes enhancing the
images and segment the images
(iii) Feature extraction and classification.

3. • As a first step, RGB images of leaf samples were taken . The
procedure for pre processing stages are given below:
1) Reading the RGB image.
2) Histogram equalization.
3) Resizing the image.
4) Create the color transformation structure.
5) K-means clustering Algorithm.
6) Applying masking in an image.
7) Convert the affected leaf (clusters) from RGB to HSI.
8) Create a spatial Gray Level Dependence Matrices (SGDM’s) for S
and H.
9) Calculating the features by calling the gray-level cooccurrence
matrix (GLCM) parameters.
10) Recognition using Support Vector Machine(SVM).

4. Histogram equalization
• Different Images of healthy and disease
affected leaves are collected from the dataset
Histogram equalization is performed on the
image in order to increase its quality prior to
clustering process as shown in figure
3.Resizing of the image performed to map the
images with equal size

5. K-means Algorithm
• Computation of disease severity requires
computation of diseased area of the leaf. This
leads to the segmentation of diseased portion of
the leaf from the healthy portion. Segmentation
partitions the image in to meaningful parts for
better analysis and understanding of the image.
Thresholding and region growing are two basic
approaches of segmentation. Thresholding is
simplest where gray image can be partitioned
based on threshold value. The image is converted
into binary image based on whether the image
pixels fall below or above

6. • the threshold value. This approach does not
provide effective segmentation of the image and
hence limits the classification of the various
objects of the image if the image contains
multiple regions or parts or color. Further it
reduces proper detection of the required area.
The region growing approach is advantageous in
such situations. K- means clustering is one of the
popular algorithm in this approach.the flow chart
for K means clustering

7. Flow Chart for K Means Clustering

8. • K-Means clustering algorithm extent to partition
of q images into p clusters and each images
belongs to the corresponding clusters with the
centroid , mean intensity and area [9]. This
algorithm introduces a p different clusters.The
good number of clusters p leading to give the
separation (distance) is not known as a prior and
it should be computed from the data set. The
important of K-Means clustering is to reduce the
total cluster variance or the square function :

9. • where,j=objective function, p=number of clusters, q=number of
cases, a=case q, c=centroid for cluster p,
• 1) Algorithm:
The procedure for K-Means Clustering Algorithm is given below:
1) Classify the images into p number of groups where p should be
known.
2) Mark p points at randomly in cluster centroid.
3) Mapping objects to their closest cluster centroid.
4) Calculate the mean, centroid or perimeter of all images in each
cluster.
5) Repeat steps 2, 3 and 4 until the equal points are mapped to each
cluster.

10. Result k means clustering
Cluster 1
Cluster 2
Cluster 3

12. Final output