ac

Evaluating Random Forest Algorithm: Detection of Palm Oil Leaf Disease

Authors

  • Oky Rahmanto Politeknik Negeri Tanah Laut, Indonesia
  • Veri Julianto Politeknik Negeri Tanah Laut, Indonesia
  • Ahmad Rusadi Arrahimi Politeknik Negeri Tanah Laut, Indonesia

DOI:

10.47709/brilliance.v4i2.4798

Keywords:

Random Forest, PCA, Palm Oil, Leaf

Dimension Badge Record



Abstract

This research investigates the application of machine learning techniques for detecting diseases in oil palm leaves, utilizing a dataset of 1,119 images sourced from plantations in the Tanah Laut district. The dataset comprises 488 diseased and 631 healthy leaf samples, which were carefully cropped to isolate leaf areas and labeled with the assistance of domain experts. For feature extraction, both Lab and RGB color spaces were considered, alongside Haralick texture features, resulting in a total of eleven features per pixel. To reduce dimensionality and select relevant features, Principal Component Analysis (PCA) and Random Forest methods were applied. Support Vector Machine (SVM) was subsequently employed for the classification of leaf health status, and model performance was evaluated using accuracy, precision, recall, and F1 score metrics, all derived from a confusion matrix. The study finds that PCA and Random Forest significantly enhance model performance, improving the ability to distinguish between healthy and diseased leaves. These findings provide valuable insights for the development of automated disease detection systems in oil palm plantations, with potential applications in precision agriculture. Additionally, the results suggest pathways for further research into plant disease diagnostics, highlighting the role of advanced machine learning techniques in enhancing crop management and supporting sustainable agricultural practices.

Google Scholar Cite Analysis
Abstract viewed = 36 times

References

Aji, A. F., Munajat, Q., Pratama, A. P., Kalamullah, H., Setiyawan, J., & Arymurthy, A. M. (2013). Detection of palm oil leaf disease with image processing and neural network classification on mobile device. International Journal of Computer Theory and Engineering, 5(3), 528.

Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M., & Saleem, B. A. (2017). Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104. https://doi.org/10.1016/j.compag.2017.04.008

Arrahimi, A. R., Julianto, V., & Rahmanto, O. (2024). Machine learning to Detect Palm Oil Diseases Based on Leaf Extraction Features and Principal Component Analysis (PCA). KLIK - KUMPULAN JURNAL ILMU KOMPUTER, 11(1), 26–36. https://doi.org/10.20527/klik.v11i1.659

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

Ichsan, M., Saputra, W., & Permatasari, A. (t.t.). OIL PALM SMALLHOLDERS ON THE EDGE: WHY BUSINESS PARTNERSHIPS NEED TO BE REDEFINED.

Kurita, T. (2021). Principal Component Analysis (PCA). Dalam K. Ikeuchi (Ed.), Computer Vision: A Reference Guide (hlm. 1013–1016). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-63416-2_649

Masazhar, A. N. I., & Kamal, M. M. (2017). Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier. 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 1–6. https://doi.org/10.1109/ICSIMA.2017.8311978

Mutlag, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H. (2020). Feature Extraction Methods: A Review. Journal of Physics: Conference Series, 1591(1), 012028. https://doi.org/10.1088/1742-6596/1591/1/012028

Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157, 270–280. https://doi.org/10.1016/j.compag.2018.12.038

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

Semangu, H. (1989). Penyakit-penyakit tanaman perkebunan di Indonesia. Gadjah Mada University Press.

Septiarini, A., Hamdani, H., Hatta, H. R., & Kasim, A. A. (2019). Image-based processing for ripeness classification of oil palm fruit. 2019 5th International Conference on Science in Information Technology (ICSITech), 23–26. https://doi.org/10.1109/ICSITech46713.2019.8987575

Septiarini, A., Hamdani, H., Junirianto, E., Thayf, M. S. S., Triyono, G., & Henderi. (2022). Oil Palm Leaf Disease Detection on Natural Background Using Convolutional Neural Networks. 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 388–392. https://doi.org/10.1109/COMNETSAT56033.2022.9994555

Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., Lali, M. I. U., & Javed, M. Y. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Computers and Electronics in Agriculture, 150, 220–234. https://doi.org/10.1016/j.compag.2018.04.023

Zheng, J., Fu, H., Li, W., Wu, W., Yu, L., Yuan, S., … Kanniah, K. D. (2021). Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 95–121. https://doi.org/10.1016/j.isprsjprs.2021.01.008

Downloads

ARTICLE Published HISTORY

Submitted Date: 2024-10-08
Accepted Date: 2024-12-11
Published Date: 2025-01-23

How to Cite

Rahmanto, O., Julianto, V., & Arrahimi, A. R. (2025). Evaluating Random Forest Algorithm: Detection of Palm Oil Leaf Disease. Brilliance: Research of Artificial Intelligence, 4(2), 919-924. https://doi.org/10.47709/brilliance.v4i2.4798