COMBINATION OF LEARNING VECTOR QUANTIZATION AND LINEAR DISCRIMINANT ANALYSIS FOR TEA LEAF DISEASE CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v10i3.6013Keywords:
GLCM texture features, linear discriminant analysis, learning vector quantization, mean color, tea leaf disease classificationAbstract
Tea farming, one of the key pillars of Indonesia's economy, faces productivity challenges due to diseases affecting tea leaves. Manual identification of tea leaf diseases requires significant time and cost, making an automated solution necessary. This research develops an innovative model for classifying tea leaf diseases by synergizing Learning Vector Quantization (LVQ) and Linear Discriminant Analysis (LDA). By leveraging LVQ’s prototype-based classification and LDA’s dimensionality reduction, the model ensures accurate and efficient disease identification. During preprocessing, tea leaf images were converted to the CIELAB color space to enhance segmentation using Otsu’s Thresholding. Features such as Mean Color and texture attributes based on Gray Level Co-occurrence Matrix (GLCM) were extracted, reduced via LDA, and classified using LVQ. Tested on five tea leaf disease classes, the model achieved 94.1% accuracy. This performance underscores its potential to significantly assist farmers in early detection and management of tea leaf diseases, while also providing researchers with a robust tool for advancing agricultural technology.
Downloads
References
Directorate of Food Crops Horticulture and Estate Crops Statistics, Indonesia Tea Statistics 2022, vol. 16. BPS-Statistics Indonesia, 2023.
A. K. Pandey, G. D. Sinniah, A. Babu, and A. Tanti, “How the Global Tea Industry Copes With Fungal Diseases – Challenges and Opportunities,” Plant Dis., vol. 105, no. 7, pp. 1868–1879, Mar. 2021, doi: 10.1094/PDIS-09-20-1945-FE.
A. M. Yatoo, M. N. Ali, Z. A. Baba, and B. Hassan, “Sustainable Management of Diseases and Pests in Crops by Vermicompost and Vermicompost Tea. A Review,” Agron. Sustain. Dev., vol. 41, no. 1, p. 7, 2021, doi: 10.1007/s13593-020-00657-w.
Y. Sari, A. R. Baskara, and R. Wahyuni, “Classification of Chili Leaf Disease Using the Gray Level Co-occurrence Matrix (GLCM) and the Support Vector Machine (SVM) Methods,” in International Conference on Informatics and Computing (ICIC), IEEE, 2021, pp. 1–4.
R. S. Septarini, M. Tonggiroh, and S. Nurhayati, “Identification of Potato Leaf Diseases Using Artificial Neural Networks With Extreme Learning Machine,” Pilar Nusa Mandiri J. Comput. Inf. Syst., vol. 20, no. 1, pp. 60–68, 2024, doi: 10.33480/pilar.v20i1.5307.
Y. Lu et al., “Recognition of rice sheath blight based on a backpropagation neural network,” Electron., vol. 10, no. 23, 2021, doi: 10.3390/electronics10232907.
H. Gunawan, S. Soeheri, D. Adhar, H. Hardianto, L. Wahyuni, and C. B. Harahap, “Learning Vector Quantization (LVQ) For Colorectal Cancer Identification Based on Microscopic Network Image,” in International Conference on Cybernetics and Intelligent System (ICORIS), 2022, pp. 1–5. doi: 10.1109/ICORIS56080.2022.10031513.
C. Diao, D. Kleyko, J. M. Rabaey, and B. A. Olshausen, “Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing,” in International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1–9. doi: 10.1109/IJCNN52387.2021.9533316.
F. Tang, P. Tiňo, and H. Yu, “Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold,” IEEE Trans. Cybern., vol. 53, no. 8, pp. 5178–5190, 2023, doi: 10.1109/TCYB.2022.3178412.
R. I. Borman, D. E. Kurniawan, Styawati, I. Ahmad, and D. Alita, “Classification of Maturity Levels of Palm Fresh Fruit Bunches Using the Linear Discriminant Analysis Algorithm,” AIP Conf. Proc., vol. 2665, no. 1, pp. 30023.1-30023.8, 2023, doi: 10.1063/5.0126513.
S. Zhao, B. Zhang, J. Yang, J. Zhou, and Y. Xu, “Linear Discriminant Analysis,” Nat. Rev. Methods Prim., vol. 4, no. 1, p. 70, 2024, doi: 10.1038/s43586-024-00346-y.
R. I. Borman, R. Napianto, N. Nugroho, D. Pasha, Y. Rahmanto, and Y. E. P. Yudoutomo, “Implementation of PCA and KNN Algorithms in the Classification of Indonesian Medicinal Plants,” in International Conference on Computer Science, Information Technology and Electrical Engineering (ICOMITEE), IEEE, 2021, pp. 46–50.
A. Abdullah and R. H. Prince, “Tea Leaf Disease Classification,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/mamun009/tea-leaf-disease-clasification
R. I. Borman, F. Rossi, D. Alamsyah, R. Nuraini, and Y. Jusman, “Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), IEEE, 2022.
S.-H. Baek, K.-H. Park, J.-S. Jeon, and T.-Y. Kwak, “Using the CIELAB Color System for Soil Color Identification Based on Digital Image Processing,” J. Korean Geotech. Soc., vol. 38, no. 5, pp. 61–71, 2022.
I. Ahmad, Y. Rahmanto, R. I. Borman, F. Rossi, Y. Jusman, and A. D. Alexander, “Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), IEEE, 2022, pp. 12–17.
Z. Y. Tan, S. N. Basah, H. Yazid, and M. J. A. Safar, “Performance analysis of Otsu thresholding for sign language segmentation,” Multimed. Tools Appl., vol. 80, no. 14, pp. 21499–21520, 2021, doi: 10.1007/s11042-021-10688-4.
R. I. Borman, F. Rossi, Y. Jusman, A. A. A. Rahni, S. D. Putra, and A. Herdiansah, “Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), IEEE, 2021, pp. 12–17.
M. Yogeshwari and G. Thailambal, “Automatic Feature Extraction and Detection of Plant Leaf Disease Using GLCM Features and Convolutional Neural Networks,” Mater. Today Proc., vol. 81, pp. 530–536, 2023, doi: https://doi.org/10.1016/j.matpr.2021.03.700.
S. Li, H. Zhang, R. Ma, J. Zhou, J. Wen, and B. Zhang, “Linear Discriminant Analysis with Generalized Kernel Constraint for Robust Image Classification,” Pattern Recognit., vol. 136, no. 109196, pp. 1–10, 2023, doi: 10.1016/j.patcog.2022.109196.
A. Sucipto and A. K. Zyen, “Linear Discriminant Analysis for Apples Fruit Variety Based on Color Feature Extraction,” in International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, 2021, pp. 184–189.
R. I. Borman, Y. Fernando, and Y. E. P. Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 339–345, 2022.
J. E. T. Akinsola, F. O. Onipede, E. A. Olajubu, and G. A. Aderounmu, “Machine Learning Enabled Image Classification Using K-Nearest Neighbour and Learning Vector Quantization,” in Soft Computing and Its Engineering Applications, K. K. Patel, K. C. Santosh, A. Patel, and A. Ghosh, Eds., Cham: Springer Nature Switzerland, 2024, pp. 148–163.
A. F. Cobantoro, F. Masykur, and K. Sussolaikah, “Performance Analysis of Alexnet Convolutional Neural Network (CNN) Architecture With Image Objects of Rice Plant Leaves,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 8, no. 2, pp. 111–116, 2023, doi: 10.33480/jitk.v8i2.4060.
M. A. Sembiring, H. Saputra, R. A. Yusda, S. Sutarman, and E. B. Nababan, “Performance of Robust Support Vector Machine Classification Model on Balanced, Imbalanced and Outliers Datasets,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 1, pp. 208–215, 2024, doi: 10.33480/jitk.v10i1.5272.
H. N. Syifa’ and A. Nugroho, “Performance of the Yolov5 Algorithm To Detect Humans in the Rear Excavator Area,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 1, pp. 197–207, 2024, doi: 10.33480/jitk.v10i1.5576.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mutasar Mutasar, Chaeroen Niesa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
