Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means
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 agricultur...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-08-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4033 |
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| author | Mila Jumarlis Mirfan |
| author_facet | Mila Jumarlis Mirfan |
| author_sort | Mila Jumarlis |
| collection | DOAJ |
| description | 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 Co-Occurrence 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 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 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. |
| first_indexed | 2024-03-08T07:07:06Z |
| format | Article |
| id | doaj.art-c0fff0f964ab480bb8a661ea93a01718 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:07:06Z |
| publishDate | 2022-08-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-c0fff0f964ab480bb8a661ea93a017182024-02-03T04:17:44ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-08-016462463110.29207/resti.v6i4.40334033Detecting Diseases on Clove Leaves Using GLCM and Clustering K-MeansMila Jumarlis0Mirfan1STAIN MAJENEUniversitas Handayani MakassarThe 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 Co-Occurrence 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 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 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.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4033k-means, glcm, image processing, clove plants, diagnosis |
| spellingShingle | Mila Jumarlis Mirfan Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) k-means, glcm, image processing, clove plants, diagnosis |
| title | Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means |
| title_full | Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means |
| title_fullStr | Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means |
| title_full_unstemmed | Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means |
| title_short | Detecting Diseases on Clove Leaves Using GLCM and Clustering K-Means |
| title_sort | detecting diseases on clove leaves using glcm and clustering k means |
| topic | k-means, glcm, image processing, clove plants, diagnosis |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4033 |
| work_keys_str_mv | AT milajumarlis detectingdiseasesoncloveleavesusingglcmandclusteringkmeans AT mirfan detectingdiseasesoncloveleavesusingglcmandclusteringkmeans |