Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest
DOI:
https://doi.org/10.35194/mji.v17i2.5939Keywords:
Classification, Feature Extraction, MobileNetV3, Random Forest, ResNet-50, Rice Leaf DiseaseAbstract
Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.References
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[2] L. Zazuli, A. Mardedi, M. Madani, and H. Hairani, “Detection of Rice Diseases using Leaf Images with Visual Geometric Group ( VGG-19 ) Architecture and Different Optimizers,” Matrik J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 25, no. 1, pp. 73–82, 2025, doi: 10.30812/matrik.v25i1.5286.
[3] G. Y. Christiawan, R. A. Putra, A. Sulaiman, E. Poerbaningtyas, S. Widyayuningtias, and K. Kunci, “Penerapan Metode Convolutional Neural Network Mengklasifikasikan Penyakit Daun Tanaman Padi ( CNN ) Dalam,” J-Intech J. Inf. Technol., no. 204, pp. 294–306, 2023, doi: https://doi.org/10.32664/j-intech.v11i2.1006.
[4] M. A. Azim, M. K. Islam, M. M. Rahman, and F. Jahan, “An effective feature extraction method for rice leaf disease classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 463–470, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16488.
[5] A. Julianto and A. Sunyoto, “A performance evaluation of convolutional neural network architecture for classification of rice leaf disease,” IAES Int. J. Artif. Intell., vol. 10, no. 4, pp. 1069–1078, 2021, doi: 10.11591/IJAI.V10.I4.PP1069-1078.
[6] B. K. Gulo and A. R. Himamunanto, “Deteksi Penyakit Tanaman Padi ( Oryza Sativa L .) Menggunakan Support Vector Machine ( SVM ) Dan Random Forest Pada Citra Daun,” Bull. Comput. Sci. Res., vol. 5, no. 2, pp. 724–733, 2025, doi: 10.47065/bulletincsr.v5i4.660.
[7] P. I. Ritharson, K. Raimond, X. A. Mary, J. Eunice, and J. Andrew, “DeepRice?: A deep learning and deep feature based classi fi cation of Rice leaf disease subtypes,” Artif. Intell. Agric., vol. 11, pp. 34–49, 2024, doi: 10.1016/j.aiia.2023.11.001.
[8] A. G. Polontalo, M. I. Abas, and W. E. Pranata, “Identifikasi Penyakit Padi Berdasarkan Citra Daun Menggunakan Arsitektur Convolutional Neural Network Kustom,” Bull. Comput. Sci. Res., vol. 5, no. 6, pp. 1371–1379, 2025, doi: 10.47065/bulletincsr.v5i6.809.
[9] A. Fathir, R. Januar, J. Indra, D. S. Kusumaningrum, and S. Faisal, “Application of Convolutional Neural Network ( CNN ) Algorithm with ResNet-101 Architecture for Monkey Pox Detection in Human,” J. Appl. Informatics Comput., vol. 9, no. 3, pp. 1006–1012, 2025.
[10] F. D. Wardhana and A. Nugroho, “Perbandingan Arsitektur MobileNetV2 dan MobileNetV3 Dalam Klasifikasi Jenis Jeruk,” J. Ilmu Komput. dan Bisnis, vol. 16, no. 1, pp. 25–34, 2025.
[11] S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm,” Inf. Process. Agric., vol. 7, no. 2, pp. 249–260, 2020, doi: 10.1016/j.inpa.2019.09.002.
[12] N. Senan, M. Aamir, R. Ibrahim, N. S. A. M. Taujuddin, and W. H. N. W. Muda, “An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 7, pp. 116–122, 2020.
[13] M. Gogoi, V. Kumar, S. A. Begum, N. Sharma, and S. Kant, “Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach,” 2023, doi: https://doi.org/10.3390/agriculture13081505.
[14] P. A. S. Rani and N. S. Singh, “Paddy Leaf Symptom-based Disease Classification Using Deep CNN with ResNet-50,” Int. J. Adv. Sci. Comput. Eng., vol. 4, no. 2, pp. 88–94, 2022, doi: 10.62527/ijasce.4.2.83.
[15] D. Priyanto and M. Innuddin, “Optimization of Random Forest for Health Data Classification Using PCA and K-Means,” Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27646–27652, 2025, doi: https://doi.org/10.48084/etasr.12976.
[16] H. Hairani, T. Widiyaningtyas, D. D. Prasetya, and A. Aminuddin, “Addressing Imbalance in Health Datasets?: A New Method NR-Clustering SMOTE and Distance Metric Modification,” Comput. Mater. Contin., vol. 82, no. 2, pp. 2931–2949, 2025, doi: 10.32604/cmc.2024.060837.
[17] S. K. Upadhyay and A. Kumar, “A novel approach for rice plant diseases classification with deep convolutional neural network,” Int. J. Inf. Technol., vol. 14, no. 1, pp. 185–199, 2022, doi: 10.1007/s41870-021-00817-5.
[18] M. Aggarwal et al., “Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification,” Agric., vol. 13, no. 5, 2023, doi: 10.3390/agriculture13050936.
[19] D. Margarita, H. Maulana, and E. P. Mandyartha, “Klasifikasi penyakit daun padi menggunakan support vector machine berdasarkan fitur mendalam (deep feature),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 4, pp. 2256–2270, 2024.
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2025-12-31
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