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

Authors

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

DOI:

https://doi.org/10.59934/jaiea.v3i3.522

Keywords:

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

Abstract

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.

Downloads

Download data is not yet available.

References

S. Sachar and A. Kumar, “Survey of feature extraction and classification techniques to identify plant through leaves,” Expert Syst Appl, vol. 167, p. 114181, Apr. 2021, doi: 10.1016/j.eswa.2020.114181.

M. M. Amlekar, A. T. Gaikwad, R. R. Manza, and P. L. Yannawar, “Leaf shape extraction for plant classification,” in 2015 International Conference on Pervasive Computing (ICPC), IEEE, Jan. 2015, pp. 1–4. doi: 10.1109/PERVASIVE.2015.7087088.

T. Nguyen Quoc and V. Truong Hoang, “Medicinal Plant identification in the wild by using CNN,” in 2020 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, Oct. 2020, pp. 25–29. doi: 10.1109/ICTC49870.2020.9289480.

K. T. Asmara, Nisyawati, and M. Silalahi, “Ethnomedicinal Plants Used by Batak Angkola Subethnic of Bulumario Village, Sipirok, South Tapanuli, North Sumatera,” in Proceedings of the International Conference on Biology, Sciences and Education (ICoBioSE 2019), Paris, France: Atlantis Press, 2020. doi: 10.2991/absr.k.200807.024.

V. P. Supriya, S. Manikandan, T. Ramakrishnan, and P. Radha, “An Efficient Approach for the Classification of Medicinal Leaves using BFO and FRVM,” Int. J. Advanced Networking and Applications, vol. 10, no. 6, pp. 4105–4112, 2019.

P. K. Thella and V. Ulagamuthalvi, “A Brief Review on Methods for Classification of Medical Plant Images,” in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, Apr. 2021, pp. 1330–1333. doi: 10.1109/ICCMC51019.2021.9418380.

F. Haque and S. Haque, “PLANT RECOGNITION SYSTEM USING LEAF SHAPE FEATURES AND MINIMUM EUCLIDEAN DISTANCE,” ICTACT Journal on Image and Video Processing, vol. 9, no. 2, pp. 1919–1925, Nov. 2018, doi: 10.21917/ijivp.2018.0272.

L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition – A review,” Information Processing in Agriculture, vol. 8, no. 1, pp. 27–51, Mar. 2021, doi: 10.1016/j.inpa.2020.04.004.

F. Mostajer Kheirkhah and H. Asghari, “Plant leaf classification using GIST texture features,” IET Computer Vision, vol. 13, no. 4, pp. 369–375, Jun. 2019, doi: 10.1049/iet-cvi.2018.5028.

C. Yang, “Plant leaf recognition by integrating shape and texture features,” Pattern Recognit, vol. 112, p. 107809, Apr. 2021, doi: 10.1016/j.patcog.2020.107809.

P. Remagnino, S. Mayo, P. Wilkin, J. Cope, and D. Kirkup, “Feature Extraction,” in Computational Botany, Berlin, Heidelberg: Springer Berlin Heidelberg, 2017, pp. 33–56. doi: 10.1007/978-3-662-53745-9_3.

X. Jiang, F. Xie, L. Liu, Y. Peng, H. Cai, and L. Li, “Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast enhanced and diffusion weighted MRI,” Oncol Lett, May 2018, doi: 10.3892/ol.2018.8805.

Downloads

Published

2024-06-15

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. https://doi.org/10.59934/jaiea.v3i3.522

Issue

Section

Articles