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