Complete Kernel Fisher Discriminant (CKFD) and Color Difference Histogram for Palm Disease
DOI:
10.33395/sinkron.v8i4.14145Keywords:
Color Difference Histogram, Complete Kernel Fisher Discriminant, Computer Vision, Histogram Neighboring Edge, Histogram Neighboring Color IndexesAbstract
Palm oil plantations play a significant role in the economy of Indonesia, supporting 16.2 million people. However, plant diseases pose a major threat to the productivity and health of palm oil crops. Early detection of these diseases is essential to prevent yield losses and mitigate damage. This study proposes the application of the Complete Kernel Fisher Discriminant (CKFD) method combined with Color Difference Histogram to classify diseases affecting oil palm fronds and leaves. The CKFD method uses a non-linear kernel transformation to improve the performance of Fisher Linear Discriminant Analysis (FLDA), while the Color Difference Histogram enhances sensitivity to color variations in different lighting conditions. Experimental results demonstrate that the CKFD method achieves superior accuracy in disease detection compared to traditional Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The proposed approach showed an average accuracy of 94.5% for detecting diseases like Curvularia sp and Cochliobolus carbonus. The combination of CKFD with Color Difference Histogram significantly reduces the impact of lighting variations on the classification results, making it a robust solution for practical deployment in palm oil plantations. This research provides an effective tool for early disease detection and management in the palm oil industry.
Downloads
References
Angin, J. T. K. P., Johan, Sukiman, Sugianto, Simarmata, B. R., & Suharjito. (2020). Face Recognition Application with the Complete Kernel Fisher Discriminant (CKFD) Method. 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), 359–364. https://doi.org/10.1109/MECnIT48290.2020.9166682
Dong, S., Wang, Z., & Zeng, L. (2016). Lithology identification using kernel Fisher discriminant analysis with well logs. Journal of Petroleum Science and Engineering, 143, 95–102. https://doi.org/10.1016/j.petrol.2016.02.017
Frandian, B., Zufria, I., & Irawan, M. D. (2022). Implementasi Certainty Factor Untuk Diagnosis Penyakit dan Hama Pada Pelepah dan Daun Kelapa Sawit Beserta Penanganannya. Journal of Information System Research (JOSH), 3(3), 159–168. https://doi.org/10.47065/josh.v3i3.1455
Hamdani, H., Septiarini, A., Sunyoto, A., Suyanto, S., & Utaminingrum, F. (2021). Detection of oil palm leaf disease based on color histogram and supervised classifier. Optik, 245, 167753. https://doi.org/10.1016/j.ijleo.2021.167753
Hasan, S., Jahan, S., & Islam, Md. I. (2022). Disease detection of apple leaf with combination of color segmentation and modified DWT. Journal of King Saud University - Computer and Information Sciences, 34(9), 7212–7224. https://doi.org/10.1016/j.jksuci.2022.07.004
Jia, J., Ruan, Q., & Jin, Y. (2016). Geometric Preserving Local Fisher Discriminant Analysis for person re-identification. Neurocomputing, 205, 92–105. https://doi.org/10.1016/j.neucom.2016.05.003
Kipli, K., Osman, S., Joseph, A., Zen, H., Awang Salleh, D. N. S. D., Lit, A., & Chin, K. L. (2023). Deep learning applications for oil palm tree detection and counting. Smart Agricultural Technology, 5, 100241. https://doi.org/10.1016/j.atech.2023.100241
Liu, G.-H., & Yang, J.-Y. (2013). Content-based image retrieval using color difference histogram. Pattern Recognition, 46(1), 188–198. https://doi.org/10.1016/j.patcog.2012.06.001
Matarneh, S., Elghaish, F., Pour Rahimian, F., Abdellatef, E., & Abrishami, S. (2024). Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification. Automation in Construction, 160, 105297. https://doi.org/10.1016/j.autcon.2024.105297
Panchal, A. V., Patel, S. C., Bagyalakshmi, K., Kumar, P., Khan, I. R., & Soni, M. (2023). Image-based Plant Diseases Detection using Deep Learning. Materials Today: Proceedings, 80, 3500–3506. https://doi.org/10.1016/j.matpr.2021.07.281
Sarkar, D., Bali, R., & Sharma, T. (2017). Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems (1st ed. edition). Apress.
Satia, G. A. W., Firmansyah, E., & Umami, A. (2022). Perancangan Sistem Identifikasi Penyakit pada Daun Kelapa Sawit (Elaeis guineensis Jacq.) dengan Algoritma Deep Learning Convolutional Neural Networks. Jurnal Ilmiah Pertanian, 19(1), 1–10. https://doi.org/10.31849/jip.v19i1.9556
Singh, C., Walia, E., & Kaur, K. P. (2018). Enhancing color image retrieval performance with feature fusion and non-linear support vector machine classifier. Optik, 158, 127–141. https://doi.org/10.1016/j.ijleo.2017.11.202
Wang, Z., Ruan, Q., & An, G. (2016). Facial expression recognition using sparse local Fisher discriminant analysis. Neurocomputing, 174, 756–766. https://doi.org/10.1016/j.neucom.2015.09.083
Wen, T., Yan, J., Huang, D., Lu, K., Deng, C., Zeng, T., Yu, S., & He, Z. (2018). Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing. Sensors (Basel, Switzerland), 18(2), 388. https://doi.org/10.3390/s18020388
Zafeiriou, S. (2012). Subspace Learning in Krein Spaces: Complete Kernel Fisher Discriminant Analysis with Indefinite Kernels. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Computer Vision – ECCV 2012 (pp. 488–501). Springer. https://doi.org/10.1007/978-3-642-33765-9_35
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2024 Johanes Terang Kita Perangin Angin, Herman, Joni
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
