RiceGuard: A Lightweight PSO-Optimized MobileNetV2 Framework for Stable Rice Leaf Disease Classification
Abstract
Rice leaf diseases such as blast, brown spot, bacterial blight, and tungro pose a serious threat to agricultural productivity, with potential yield losses exceeding 50% under epidemic conditions. Therefore, rapid and accurate early detection is essential to support sustainable food security and precision agriculture. This study proposes a rice leaf disease classification system based on the MobileNetV2 architecture optimized using Particle Swarm Optimization (PSO). A dataset consisting of four rice leaf disease classes, collected from public repositories and sources representing real field conditions, is used to evaluate the proposed approach. Transfer learning is employed to improve training efficiency, while PSO is applied to optimize key hyperparameters to enhance model stability and convergence.
Experimental results show that MobileNetV2 optimized with PSO consistently achieves superior classification performance and improved training stability compared to the standard MobileNetV2 baseline. The baseline MobileNetV2 achieves 92% accuracy, with the highest F1-score of 0.99 on the tungro class and the lowest performance of 0.88 on the blast class. In contrast, MobileNetV2–PSO demonstrates a significant improvement, reaching 99% accuracy, with F1-scores of 0.98 for bacterial blight, 0.98 for blast, 0.99 for brown spot, and 1.00 for tungro. The largest improvement occurs in the blast class, with a 10-point increase in F1-score, indicating that PSO optimization provides greater sensitivity to complex disease patterns that were previously difficult to classify.These findings indicate that the proposed framework provides an accurate and lightweight solution with strong potential for deployment on mobile and resource-constrained platforms to support intelligent rice disease diagnosis.
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Ahad, M. T., Li, Y., Song, B., & Bhuiyan, T. (2023). Comparison of CNN-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture, 9, 22–35. https://doi.org/10.1016/j.aiia.2023.07.001
Akay, M., et al. (2021). Deep learning classification of systemic sclerosis skin using the MobileNetV2 model. IEEE Open Journal of Engineering in Medicine and Biology, 2, 104–110. https://doi.org/10.1109/OJEMB.2021.3066097
Anamisa, D. R., Rachmad, A., Yusuf, M., Jauhari, A., Erdiansa, R. D. T., & Hariyawan, M. Y. (2021). Classification of diseases for rice plant based on naive bayes classifier with a combination of promethee. Communications in Mathematical Biology and Neuroscience, 2021. https://doi.org/10.28919/cmbn/6674
Annur, I. F., Umami, J., Annafii, M. N., Trisnaningrum, N., & Putra, O. V. (2023). Klasifikasi tingkat keparahan penyakit leafblast tanaman padi menggunakan MobileNetV2. Fountain of Informatics Journal, 8(1), 7–14. https://doi.org/10.21111/fij.v8i1.9419
Arafat, F. A., Ichsan, M. N., & Pramoedya, M. F. (2025). Pemanfaatan arsitektur MobileNet-CNN untuk mendiagnosis penyakit pada daun singkong melalui teknologi citra digital. Prosiding SEMNAS INOTEK.
Babu, S., Maravarman, M., & Pitchai, R. (2022). Detection of rice plant disease using deep learning techniques. Journal of Mobile Multimedia, 18(3), 757–770. https://doi.org/10.13052/jmm1550-4646.18314
Chen, H., Zhou, G., He, W., Duan, X., & Jiang, H. (2024). Classification and identification of agricultural products based on improved MobileNetV2. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-53349-w
Chen, J., Zhang, D., Nanehkaran, Y. A., & Li, D. (2020). Detection of rice plant diseases based on deep transfer learning. Journal of the Science of Food and Agriculture, 100(7), 3246–3256. https://doi.org/10.1002/jsfa.10365
Daniya, T., Vidyadhari, C., & Aluri, S. (2022). Rice plant disease detection using sensing recognition strategy based on artificial intelligence. Journal of Mobile Multimedia, 18(3), 705–722. https://doi.org/10.13052/jmm1550-4646.18311
Elfatimi, E., Eryigit, R., & Elfatimi, L. (2022). Beans leaf diseases classification using MobileNet models. IEEE Access, 10, 9471–9482. https://doi.org/10.1109/ACCESS.2022.3142817
Fathy, A., Abdelkader, H., Chelloug, S. A., Muthanna, M. S. A., & Abdelatey, A. (2025). A hybrid PSO-GA optimized approach for COVID-19 detection using CT scan. IEEE Access, 13, 109345–109364. https://doi.org/10.1109/ACCESS.2025.3581852
Ferdinand, Y., & Al Maki, W. F. (2022). Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection. International Journal of Advances in Intelligent Informatics, 8(3), 337–348. https://doi.org/10.26555/ijain.v8i3.951
Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences, 115(18), 4613–4618. https://doi.org/10.1073/pnas.1716999115
Hartojo, J., Bastiaans, J. C., Pramunendar, R. A., & Andono, N. (2024). Enhancing support vector machine classification of nutrient deficiency in rice plants through particle swarm optimization-based feature selection. International Journal of New Media Technology, 11(2), 100.
Hasan, M. M., Uddin, A. F. M. S., Akhond, M. R., Uddin, M. J., Hossain, M. A., & Hossain, M. A. (2023). Machine learning and image processing techniques for rice disease detection: A critical analysis. International Journal of Plant Biology, 14(4). https://doi.org/10.3390/ijpb14040087
Hawari, F. H., Fadillah, F., Alviandi, M. R., & Arifin, T. (2022). Klasifikasi penyakit padi menggunakan algoritma CNN (convolutional neural network). JURNAL RESPONSIF, 4(2), 184–189.
Joseph, D. S., Pawar, P. M., & Chakradeo, K. (2024). Real-time plant disease dataset development and detection of plant disease using deep learning. IEEE Access, 12, 16310–16333. https://doi.org/10.1109/ACCESS.2024.3358333
Mathulaprangsan, S., Patarapuwadol, S., Lanthong, K., Jetpipattanapong, D., & Sateanpattanakul, S. (2021). Rice disease recognition using effective deep neural networks. Journal of Web Engineering, 20(4), 853–878. https://doi.org/10.13052/jwe1540-9589.20313
Mekha, P., & Teeyasuksaet, N. (2021). Image classification of rice leaf diseases using random forest algorithm. In Proceedings of the 2021 Joint 6th International Conference on Digital Arts, Media and Technology with 4th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering (ECTI DAMT and NCON 2021) (pp. 165–169). IEEE. https://doi.org/10.1109/ECTIDAMTNCON51128.2021.9425696
Pan, H., Pang, Z., Wang, Y., Wang, Y., & Chen, L. (2020). A new image recognition and classification method combining transfer learning algorithm and MobileNet model for welding defects. IEEE Access, 8, 119951–119960. https://doi.org/10.1109/ACCESS.2020.3005450
Patil, R. R., & Kumar, S. (2022a). Rice transformer: A novel integrated management system for controlling rice diseases. IEEE Access, 10, 87698–87714. https://doi.org/10.1109/ACCESS.2022.3200688
Patil, R. R., & Kumar, S. (2022b). Rice-fusion: A multimodality data fusion framework for rice disease diagnosis. IEEE Access, 10, 5207–5222. https://doi.org/10.1109/ACCESS.2022.3140815
Putra, O. V., Mustaqim, M. Z., & Muriatmoko, D. (2023). Transfer learning untuk klasifikasi penyakit dan hama padi menggunakan MobileNetV2. Jurnal Teknik Informatika.
Putra, O. V., Trisnaningrum, N., Puspitasari, N. S., Wibowo, A. T., & Rachmawaty, E. (2022). HiT-LIDIA: A framework for rice leaf disease classification using ensemble and hierarchical transfer learning. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, 13(3), 196. https://doi.org/10.24843/lkjiti.2022.v13.i03.p06
Richter, D. J., Bappi, M. I., Kolekar, S. S., & Kim, K. (2025). A systematic review of the current state of transfer learning accelerated CNN-based plant leaf disease classification. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3584404
Saputra, R. A., Wasyianti, S., Supriyatna, A., & Saefudin, D. F. (2021). Penerapan algoritma convolutional neural network dan arsitektur MobileNet pada aplikasi deteksi penyakit daun padi. JURNAL SWABUMI, 9(2). https://archive.ics.uci.edu/ml/datasets/Rice
Sethy, P. K., Barpanda, N. K., Rath, A. K., & Behera, S. K. (2020). Deep feature based rice leaf disease identification using support vector machine. Computers and Electronics in Agriculture, 175, Article 105527. https://doi.org/10.1016/j.compag.2020.105527
Shalehah, Mazdadi, M. I., Farmadi, A., Kartini, D., & Muliadi. (2023). Implementation of particle swarm optimization feature selection on Naïve Bayes for thoracic surgery classification. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 5(3), 150–158. https://doi.org/10.35882/jeemi.v5i3.305
Shidik, G. F., et al. (2024). Bird species classification enhancement via adaptive inertia weight particle swarm optimization-based image augmentation selection. IEEE Access, 12, 197048–197060. https://doi.org/10.1109/ACCESS.2024.3521455
Singh, S. P., Pritamdas, K., Devi, K. J., & Devi, S. D. (2022). Custom convolutional neural network for detection and classification of rice plant diseases. Procedia Computer Science, 2026–2040. https://doi.org/10.1016/j.procs.2023.01.179
Tejaswini, P., Singh, P., Ramchandani, M., Rathore, Y. K., & Janghel, R. R. (2022). Rice leaf disease classification using CNN. IOP Conference Series: Earth and Environmental Science, 1032(1), Article 012017. https://doi.org/10.1088/1755-1315/1032/1/012017
Vasantha, S. V., Samreen, S., & Aparna, Y. L. (2022). Rice disease diagnosis system (RDDS). Computers, Materials and Continua, 73(1), 1895–1914. https://doi.org/10.32604/cmc.2022.028504
Wang, Y., Wang, H., & Peng, Z. (2022). Rice diseases detection and classification using attention based neural network and Bayesian optimization. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2021.114770
DOI: https://doi.org/10.20527/cetj.v6i1.18663
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