Syahputra, Hermawan and Indra, Zulfahmi and Febrian, Didi and Andriani, Dhea Putri (2019) LEAF FEATURE EXTRACTION USING GLCM, MOMENT INVARIANT AND SHAPE MORPHOLOGY FOR INDONESIAN MEDICINAL PLANTS RECOGNITION. In: The 3rd International Conference on Mathematics, Sciences, Education, and Technology, 4-5 Oct 2018, Padang.
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Abstract
This study aims to determine the extraction of GLCM texture features, shape morphology and moment invariant features on the leaf image of medicinal plants and determine the accuracy of plant recognition based on these three features by using Artificial Neural Network Classifiers. The procedure performed to classify medicinal plants based on their leaf image is image acquisition, image pre-processing, feature extraction, image classification and calculating the accuracy of test results. The introduction had tested for ten Indonesian medicinal plant samples, namely: Bangun-Bangun, Binahong, Jarak, Kemuning, Mangkokan, Mengkudu, Pegagan, Sambiloto, Sambung Nyawa, and Sirih. Based on the test results, obtained 97% accuracy with GLCM features, 69% with Shape Morphological features, 86% with GLCM and Shape Morphological features and 79% with moment invariant features.
Item Type: | Conference or Workshop Item (Paper) |
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Keywords: | Medicinal plant; Leaf feature extraction; Digital image; Plant morphology |
Subjects: | Q Science > Q Science (General) Q Science > QK Botany > QK640 Plant anatomy R Medicine > RS Pharmacy and materia medica > RS153 Materia medica > RS160 Pharmacognosy. Pharmaceutical substances (Plant, animal, and inorganic) |
Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Matematika |
Depositing User: | Mrs Harly Christy Siagian |
Date Deposited: | 08 May 2020 08:06 |
Last Modified: | 08 May 2020 08:06 |
URI: | https://digilib.unimed.ac.id/id/eprint/39262 |