Recognition of Medicinal Plant Leaf Patterns Using Morphology-Based and GLCM Feature Extraction


  • Imran Lubis Universitas Harapan Medan
  • Tommy Universitas Harapan Medan
  • Rosyidah Siregar Universitas Harapan Medan



Gray-Level Co-occurrence Matrix, Morphological Features, Medicinal Plant Leaves, Pattern Recognition


This research aims to develop a medicinal plant leaf pattern recognition system using morphological feature extraction and GLCM (Gray-Level Co-occurrence Matrix). This approach utilizes a combination of morphological features that describe the shape and structure of the leaves, as well as texture features that capture the surface patterns of the leaves. A diverse dataset was collected, and features such as area, perimeter, aspect ratio, circularity, and Hu Moments were extracted for morphological description. Meanwhile, texture features such as contrast, dissimilarity, homogeneity, and energy were extracted using GLCM. An Artificial Neural Network model was then trained and evaluated using precision, recall, and F1-score metrics. The research results indicate that the combination of morphological and texture features enhances the accuracy of leaf pattern recognition, with the model achieving an accuracy of 87% on the test dataset. This system has the potential for applications in the health sector, pharmaceuticals, biodiversity conservation, and education.


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How to Cite

Lubis, I., Tommy, & Rosyidah Siregar. (2024). Recognition of Medicinal Plant Leaf Patterns Using Morphology-Based and GLCM Feature Extraction. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 3(3), 834–839.