Introducing the Mutated Binary Whale Optimization Algorithm for Rice Leaf Disease Classification
Received: 7 August 2025 | Revised: 26 September 2025 | Accepted: 5 October 2025 | Online: 8 December 2025
Corresponding author: Sutikno
Abstract
Rice leaf diseases can reduce yields, making early detection crucial. Machine learning has played a significant role in identifying and classifying these diseases. Combining appropriate descriptors for feature extraction has been shown to improve accuracy but increases classification time. Reducing classification time can be achieved through feature selection. One wrapper feature selection method that has exploration and exploitation capabilities to solve optimization problems is the Binary Whale Optimization Algorithm (BWOA). However, this feature selection method is prone to get stuck in local solutions when facing high-dimensional search spaces. This study proposes a new feature selection algorithm, an improvement on BWOA, to address this problem by incorporating a mutation process. The proposed method is called the Mutated Binary Whale Optimization Algorithm (MBWOA). Based on experimental evidence, the proposed method can reduce both fitness and classification time without compromising accuracy, but even improving it. Additionally, the proposed method exhibits faster convergence than BWOA. The proposed method achieves 100% accuracy on the RLD1 dataset and 99.41% accuracy on the RLD2 dataset. Therefore, the proposed method is relatively suitable for classifying or detecting rice leaf diseases.
Keywords:
improvement of BWOA, mutation algorithm, MBWOA, rice leaf disease classificationDownloads
References
"Rice | USDA Foreign Agricultural Service." https://www.fas.usda.gov/data/production/commodity/0422110.
N. Jiang et al., "Resistance Genes and their Interactions with Bacterial Blight/Leaf Streak Pathogens (Xanthomonas oryzae) in Rice (Oryza sativa L.)—an Updated Review," Rice, vol. 13, no. 1, Dec. 2020, Art. no. 3. DOI: https://doi.org/10.1186/s12284-019-0358-y
B. S. Anami, N. N. Malvade, and S. Palaiah, "Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images," Artificial Intelligence in Agriculture, vol. 4, pp. 12–20, 2020. DOI: https://doi.org/10.1016/j.aiia.2020.03.001
N. Krishnamoorthy, L. V. N. Prasad, C. S. P. Kumar, B. Subedi, H. B. Abraha, and V. E. Sathushjunar, "Rice leaf diseases prediction using deep neural networks with transfer learning," Environmental Research, vol. 198, Jul. 2021, Art. no. 111275. DOI: https://doi.org/10.1016/j.envres.2021.111275
R. Yakkundimath, G. Saunshi, B. Anami, and S. Palaiah, "Classification of Rice Diseases using Convolutional Neural Network Models," Journal of The Institution of Engineers (India): Series B, vol. 103, no. 4, pp. 1047–1059, Aug. 2022. DOI: https://doi.org/10.1007/s40031-021-00704-4
C. J. N. Canlas et al., "Camera Radial Distance-Based Accuracy of a Bacterial Blight, Brown Spot, and Rice Blast Plant Disease Identification System for Remote Communications," Journal of Image and Graphics, vol. 10, no. 4, 2022. DOI: https://doi.org/10.18178/joig.10.4.158-165
C. G. Simhadri and H. K. Kondaveeti, "Automatic Recognition of Rice Leaf Diseases Using Transfer Learning," Agronomy, vol. 13, no. 4, Mar. 2023, Art. no. 961. DOI: https://doi.org/10.3390/agronomy13040961
J. Deng et al., "Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone," Agronomy, vol. 13, no. 8, Aug. 2023, Art. no. 2139. DOI: https://doi.org/10.3390/agronomy13082139
D. R. F. R. Anandhi and S. Sathiamoorthy, "Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11959–11964, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6324
M. E. Pothen and M. L. Pai, "Detection of Rice Leaf Diseases Using Image Processing," in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, Mar. 2020, pp. 424–430. DOI: https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00080
D. R. Sulistyaningrum, A. Rasyida, and B. Setiyono, "Rice disease classification based on leaf image using multilevel Support Vector Machine (SVM)," Journal of Physics: Conference Series, vol. 1490, no. 1, Mar. 2020, Art. no. 012053. DOI: https://doi.org/10.1088/1742-6596/1490/1/012053
D. Zhao et al., "Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm," Frontiers in Plant Science, vol. 13, May 2022, Art. no. 879668. DOI: https://doi.org/10.3389/fpls.2022.879668
Sutikno, A. Harjoko, and Afiahayati, "Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors," International Journal of Intelligent Engineering & Systems, vol. 15, no. 1, pp. 428–441, 2022. DOI: https://doi.org/10.22266/ijies2022.0228.39
B. Remeseiro and V. Bolon-Canedo, "A review of feature selection methods in medical applications," Computers in Biology and Medicine, vol. 112, Sep. 2019, Art. no. 103375. DOI: https://doi.org/10.1016/j.compbiomed.2019.103375
J. Zhang, Y. Xiong, and S. Min, "A new hybrid filter/wrapper algorithm for feature selection in classification," Analytica Chimica Acta, vol. 1080, pp. 43–54, Nov. 2019. DOI: https://doi.org/10.1016/j.aca.2019.06.054
S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51–67, May 2016. DOI: https://doi.org/10.1016/j.advengsoft.2016.01.008
M. Abdel-Basset, D. El-Shahat, I. El-henawy, V. H. C. de Albuquerque, and S. Mirjalili, "A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection," Expert Systems with Applications, vol. 139, Jan. 2020, Art. no. 112824. DOI: https://doi.org/10.1016/j.eswa.2019.112824
Sutikno, A. Harjoko, and Afiahayati, "Fusion of BIFFOA and Adaptive Two-Phase Mutation for Helmetless Motorcyclist Detection," International Journal of Advanced Computer Science and Applications, vol. 13, no. 1, 2022. DOI: https://doi.org/10.14569/IJACSA.2022.0130170
T. Setiady, "Leaf Rice Disease." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/tedisetiady/leaf-rice-disease-indonesia.
"Rice Leaf Diseases." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/esc2023/rice-leaf-diseases.
R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural Features for Image Classification," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, Nov. 1973. DOI: https://doi.org/10.1109/TSMC.1973.4309314
L. K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 2, pp. 780–795, Mar. 1999. DOI: https://doi.org/10.1109/36.752194
T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, no. 1, pp. 51–59, Jan. 1996. DOI: https://doi.org/10.1016/0031-3203(95)00067-4
M. J. Swain and D. H. Ballard, "Color indexing," International Journal of Computer Vision, vol. 7, no. 1, pp. 11–32, Nov. 1991. DOI: https://doi.org/10.1007/BF00130487
Sutikno, H. A. Wibawa, and D. H. Ballard, "Improving Classification Performance of Rice Leaf Diseases using the Combination of GLCM, LBP, and CH Descriptors." 2025.
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001. DOI: https://doi.org/10.1023/A:1010933404324
P. Mekha and N. Teeyasuksaet, "Image Classification of Rice Leaf Diseases Using Random Forest Algorithm," in 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Cha-am, Thailand, Mar. 2021, pp. 165–169. DOI: https://doi.org/10.1109/ECTIDAMTNCON51128.2021.9425696
S. M. Shahidur Harun Rumy, Md. I. Arefin Hossain, F. Jahan, and T. Tanvin, "An IoT based System with Edge Intelligence for Rice Leaf Disease Detection using Machine Learning," in 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, Canada, Apr. 2021, pp. 1–6. DOI: https://doi.org/10.1109/IEMTRONICS52119.2021.9422499
M. Mafarja and S. Mirjalili, "Whale optimization approaches for wrapper feature selection," Applied Soft Computing, vol. 62, pp. 441–453, Jan. 2018. DOI: https://doi.org/10.1016/j.asoc.2017.11.006
K. Widarsono, A. Soeprijanto, and R. S. Wibowo, "Improved Whale Optimization Algorithm for Dynamic Optimal Power Flow with Renewable Energy Penetration," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 20379–20387, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9662
Y. Hou, J. Li, H. Yu, and Z. Li, "BIFFOA: A Novel Binary Improved Fruit Fly Algorithm for Feature Selection," IEEE Access, vol. 7, pp. 81177–81194, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2917502
M. Paliwal and U. A. Kumar, "Neural networks and statistical techniques: A review of applications," Expert Systems with Applications, vol. 36, no. 1, pp. 2–17, Jan. 2009. DOI: https://doi.org/10.1016/j.eswa.2007.10.005
S. S. Pandi, K. D. Kumar, K. Raja, and A. Senthilselvi, "Rice Plant Leaf Disease Classification using Machine Learning Algorithm," in 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, May 2024, pp. 59–63. DOI: https://doi.org/10.1109/InCACCT61598.2024.10551249
Downloads
How to Cite
License
Copyright (c) 2025 Sutikno, Helmie Arif Wibawa, Indra Waspada
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
