Vol. 2 No. 2 (2026): Vol 2 No 2 (November 2026)
Articles

A Comparative Study of Machine Learning Algorithms for Plant Leaf Disease Classification Using Hybrid Feature

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  • Ulfia Rahmah,
  • Dini Rivani,
  • Muhammad Ariel,
  • Muh Subhan
Ulfia Rahmah
Politeknik Negeri lhokseumawe
Dini Rivani
Politeknik Negeri lhokseumawe
Muhammad Ariel
Universitas Syiah Kuala
Muh Subhan
Universitas Jambi

Published 10-11-2026

Keywords

  • Plant Leaf Disease Classification,
  • Color Moments,
  • Gray Level Co-occurrence Matrix ,
  • Local Binary Pattern ,
  • Synthetic Minority Oversampling Technique (SMOTE)

Copyright (c) 2026 Ulfia Rahmah, Dini Rivani, Muhammad Ariel, Muh Subhan (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite

[1]
“A Comparative Study of Machine Learning Algorithms for Plant Leaf Disease Classification Using Hybrid Feature”, IJAIC, vol. 2, no. 2, pp. 74–85, Nov. 2026, doi: 10.67745/ijaic.v2i2.29.

Abstract

Plant leaf diseases are among the major factors contributing to reduced agricultural productivity and crop quality. Manual disease identification is time-consuming and requires considerable experience and accuracy, making it less efficient when applied on a large scale. Advances in image processing and machine learning offer opportunities to develop automated plant disease classification systems based on leaf images. This study aims to compare the performance of Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes algorithms in classifying plant leaf diseases using color, texture, and shape features. The dataset used in this study is the Plant Leaf Dataset, which consists of 3,695 leaf images across four classes: Tomato Healthy, Tomato Leaf Mold, Potato Healthy, and Potato Late Blight. Color features were extracted using Color Moments in the RGB and HSV color spaces, while texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). Shape features were extracted using contour analysis and Hu Moments. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. The experimental results show that SVM achieved the highest accuracy of 97%, followed by Random Forest and KNN, both at 95%, while Naive Bayes achieved an accuracy of 57.10%. These findings indicate that SVM is the most effective algorithm for plant leaf disease classification on the dataset used in this study.

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References

  1. M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” 2023, Frontiers Media SA. doi: 10.3389/fpls.2023.1158933.
  2. I. Pacal et al., “A systematic review of deep learning techniques for plant diseases,” Artif. Intell. Rev., vol. 57, no. 11, Nov. 2024, doi: 10.1007/s10462-024-10944-7.
  3. P. Tm, A. Pranathi, K. Saiashritha, N. B. Chittaragi, and S. G. Koolagudi, “Tomato Leaf Disease Detection Using Convolutional Neural Networks,” in 2018 11th International Conference on Contemporary Computing, IC3 2018, Institute of Electrical and Electronics Engineers Inc., Nov. 2018. doi: 10.1109/IC3.2018.8530532.
  4. U. B. Gewali, S. T. Monteiro, and E. Saber, “Machine learning based hyperspectral image analysis: A survey,” 2018.
  5. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, International Conference on Learning Representations, ICLR, 2021.
  6. V. Gautam, H. Maheshwari, R. G. Tiwari, A. K. Agarwal, N. K. Trivedi, and N. Garg, “Feature Fusion-Based Deep Learning Model to Ensure Worker Safety at Construction Sites,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ACET61898.2024.10730698.
  7. S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, no. September, Sep. 2016, doi: 10.3389/fpls.2016.01419.
  8. A. Singh and H. Kaur, “Potato plant leaves disease detection and classification using machine learning methodologies,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Jan. 2021. doi: 10.1088/1757-899X/1022/1/012121.
  9. T. O. Emmanuel, “PlantVillage Dataset,” https://www.kaggle.com/datasets/emmarex/plantdisease.
  10. S. Saponara and A. Elhanashi, “Impact of Image Resizing on Deep Learning Detectors for Training Time and Model Performance,” in Lecture Notes in Electrical Engineering, Springer Science and Business Media Deutschland GmbH, 2022, pp. 10–17. doi: 10.1007/978-3-030-95498-7_2.
  11. I. Corley, C. Robinson, R. Dodhia, J. M. L. Ferres, and P. Najafirad, “Revisiting Pre-trained Remote Sensing Model Benchmarks: Resizing and Normalization Matters,” https://openaccess.thecvf.com/content/CVPR2024W/PBVS/html/Corley_Revisiting_Pre-trained_Remote_Sensing_Model_Benchmarks_Resizing_and_Normalization_Matters_CVPRW_2024_paper.html.
  12. H. Mansourifar and W. Shi, “Deep Synthetic Minority Over-Sampling Technique,” Mar. 2020.
  13. L. Wang, M. Han, X. Li, N. Zhang, and H. Cheng, “Review of Classification Methods on Unbalanced Data Sets,” IEEE Access, vol. 9, pp. 64606–64628, 2021, doi: 10.1109/ACCESS.2021.3074243.
  14. N. Bhosle and M. Kokare, “Random forest-based active learning for content-based image retrieval,” International Journal of Intelligent Information and Database Systems, vol. 13, pp. 72–88, 2020, doi: 10.1504/IJIIDS.2020.108223.
  15. S. H. S. Al-Kilidar and L. E. George, “Texture Classification Using Gradient Features with Artificial Neural Network,” Journal of Southwest Jiaotong University, vol. 55, no. 1, 2020, doi: 10.35741/issn.0258-2724.55.1.13.
  16. N. Alpaslan and K. Hanbay, “Multi-resolution intrinsic texture geometry-based local binary pattern for texture classification,” IEEE Access, vol. 8, pp. 54415–54430, 2020, doi: 10.1109/ACCESS.2020.2981720.
  17. M. W. Nasrudin, N. S. Yaakob, N. A. Abdul Rahim, M. Z. Zahir Ahmad, N. Ramli, and M. S. Aziz Rashid, “Moment Invariants Technique for Image Analysis and Its Applications: A Review,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jul. 2021. doi: 10.1088/1742-6596/1962/1/012028.
  18. J. A. Pruneski and A. Pareek, “Support Vector Machines,” Hand Clin., vol. 42, pp. 19–25, Feb. 2026, doi: 10.1016/j.hcl.2025.08.003.
  19. J. Klusowski, “Sharp Analysis of a Simple Model for Random Forests,” https://proceedings.mlr.press/v130/klusowski21b.html.
  20. W. Xu, Z. Yuan, and Z. Liu, “Feature Selection for Unbalanced Distribution Hybrid Data Based on k-Nearest Neighborhood Rough Set,” IEEE Transactions on Artificial Intelligence, vol. 5, pp. 229–243, Jan. 2024, doi: 10.1109/TAI.2023.3237203.
  21. S. Pathak, S. J. Quraishi, A. Singh, M. Singh, K. Arora, and D. Ather, “A Comparative Analysis of Machine Learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics,” in Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 790–795. doi: 10.1109/ICTACS59847.2023.10390255.