Klasifikasi Penyakit Pada Daun Stroberi Menggunakan K-Means Clustering dan Jaringan Syaraf Tiruan

Efrilla, Alif V.; Sulistyo, Susanto Budi; Wijaya, Krissandi; Kuncoro, Purwoko Hari; Sudarmaji, Arief

doi:10.21776/ub.jkptb.2020.008.02.06

Cited by 1 publication

(3 citation statements)

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“…For this reason, a digital image processing system is needed to help farmers detect diseases that attack their clove plants. One of the advantages of digital image processing is that it can extract certain features from images for analysis so that diseases can be distinguished from one another only by the image [6]. In 2018, clove production increased again with total production reaching 535.09 tons [3].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, many technologies have been used to identify diseases in various plants. Among the technologies, image processing is one of the technologies that is developing rapidly in identifying or diagnosing a disease in plants [6]. Image processing is needed because it can extract certain features from an image to be analyzed further so that diseases can be distinguished from one another only from an image.…”

Section: Introductionmentioning

confidence: 99%

“…There have been many previous studies attempting to identify the types of diseases in plants by digital image processing [6], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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

Jumarlis

Mirfan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%