Detecting Diseases on Clove Leaves Using GLCM and Clustering
K-Means

Detecting Diseases on Clove Leaves Using GLCM and Clustering
K-Means

K-Means, GLCM, Image Processing, Clove Plants, Diagnosis.

The detection of disease in clove plant leaves is generally carried out by diagnosing the symptoms that appear on clove plants.
This diagnosis is conducted by clove farmers only by relying on their experience or even having to seek information from other
clove farmers. This is because the agricultural sector has no disease detection system for clove leaves by utilizing digital image
processing technology to detect diseases in clove leaves. In this study, the researchers applied two methods to make it easier
for clove farmers to diagnose diseases in their clove plants. Those methods were the imaging system using Gray Level CoOccurrence Matrix (GLCM) and disease clustering using the K-Means algorithm. The objective of this study was to design and
build image pattern recognition by utilizing 4 features of the Gray Level Co-Occurrence Matrix (GLCM): energy, entropy,
homogeneity, and contrast. These 4 features were used to obtain the extraction value from an image. The outcomes were then
used to cluster the clove plant diseases using the K-Means method. In making the software, the researchers used Javascript,
HTML, CSS, PHP, and MySql to create a database. The output in this study was an information system application that provides
disease-type clustering using the K-Means algorithm. The results of the Gray Level Co-occurrence Matrix (GLCM) concerning
extracting images of clove plant leaves affected by disease indicated that the created system can be used to help clove farmers
in diagnosing what diseases are infecting their plants by only uploading photos from affected leaves of the clove plant.
Furthermore, the results of the K-Means calculation on the examined data showed several categories of Anthracnose leaf spot
diseases. In addition, sample number #40 was included in cluster 2 status, in which the average values for energy, entropy,
homogeneity, and contrast were 0.583, 0.175, 0.939, and 0.175, respectively

Mila Jumarlis1
, Mirfan2

STAIN Majene

30-08-2022

Fajar bagus W

PDF

Indonesia

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