Enhancing Rice Disease Identification using Hybrid GLCM-XGBoost with SMOTE Imbalance Handling
DOI:
https://doi.org/10.35335/z9q64978Keywords:
Disease Classification, GLCM, Rice Disease, SMOTE, XGBoostAbstract
Rice (Oryza sativa ) is a major food staple, which is prone to multiple diseases that will dramatically decrease the harvest yield. Disease identification is time consuming and is usually subject to subjective errors in a manual approach. The following research will seek to increase the level of precision of automatic rice plant disease detection, namely the Brown Spot, Hispa, and Leaf Blast classes. The suggested method combines both the Gray Level Co-occurrence Matrix (GLCM) to extract texture features and the Extreme Gradient Boosting (XGBoost) classification algorithm. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance within the dataset of 5,548 images. Preprocessing steps include resizing, grayscale conversion, and Min-Max normalization. Experimental results demonstrate that the model trained on SMOTE-balanced data with optimized XGBoost parameters achieved a superior accuracy of 98%, outperforming the imbalanced scenario (97%) and previous studies. This research confirms that the combination of GLCM, SMOTE, and XGBoost constitutes a robust and high-precision method for rice disease identificationReferences
Aida, W., & Wan, N. (2025). IoT Agri-Care Advisor Mobile Application for Monitoring Paddy Plant Health and Delivering Smart Farmer Advisory Toward Sustainable Agriculture Aplicación Móvil IoT Agri-Care para el monitoreo de la salud del cultivo de arroz y la entrega de asesoramiento inteligente al agricultor hacia una agricultura sostenible. https://doi.org/10.56294/saludcyt20251979
Alabbasi, H. A., Abdulkarem, A., & Alrammahi, H. (2025). Detection and Classification of Rice Plant Diseases Using Fusion Deep and Texture Features. 14(5). https://doi.org/10.18178/ijeetc.14.5.323-330
Anggiratih, E., Siswanti, S., Octaviani, S. K., & Sari, A. (2021). Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning. Jurnal Ilmiah SINUS, 19(1), 75. https://doi.org/10.30646/sinus.v19i1.526
Barburiceanu, S., Meza, S., Orza, B., & Malutan, R. (2021). Convolutional Neural Networks for Texture Feature Extraction . Applications to Leaf Disease Classification in Precision Agriculture. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2021.3131002
De Silva, M., & Brown, D. (2023). Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches. Sensors, 23(20). https://doi.org/10.3390/s23208531
Dubey, R. K., & Choubey, D. K. (2024). RETRACTED ARTICLE: Feature selection with Optimized XGBoost model-based paddy plant leaf disease classification. Multimedia Tools and Applications, 83, 80281. https://api.semanticscholar.org/CorpusID:267990380
Hutauruk, A. R. (2025). Smart Rice Disease Detection Based on Leaf Analysis Using the YOLO Algorithm with an Interactive User Interface. 08(02), 188–190.
Jordy, R., & Ariatmanto, D. (2025). Perbandingan Metode Ekstraksi Fitur LBP , GLCM , dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN. 14(02), 44–51. https://doi.org/10.52771/bangkitindonesia.v14i2.452
Khalil, W., Irsan, M., & Fathoni, M. F. (2024). Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method. 8(2), 974–982.
Khoiruddin, M., & Tena, S. (2024). Fruit and Vegetable Classification using Convolutional Neural Network with MobileNetV2. 2(2), 203–210. https://doi.org/10.61098/jarcis.v2i2.197
Kulkarni, P., & Shastri, S. (n.d.). Registered under MSME Government of India Rice Leaf Diseases Detection Using Machine Learning. 10, 17–22.
Kusanti, J., Penyakit, K., Padi, D., & Haris, A. (2018). Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut. Jurnal Informatika: Jurnal Pengembangan IT (JPIT), 03(01), 1–6.
Miftahushudur, T., Sahin, H. M., & Grieve, B. (2025). A Survey of Methods for Addressing Imbalance Data Problems in Agriculture Applications. 1–32.
Milano, A. C., Yasid, A., & Wahyuningrum, R. T. (2024). KLASIFIKASI PENYAKIT DAUN PADI MENGGUNAKAN MODEL DEEP LEARNING EFFICIENTNET-B6. 12(1).
Nata, H., Pirnando, N., & Petrus, J. (2025). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network dengan Arsitektur AlexNet. 207–214.
Priyangka, A. A. J. V., & Kumara, I. M. S. (2021). Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 123. https://doi.org/10.24843/lkjiti.2021.v12.i02.p06
Ramli, R., & Riadi, A. A. (2025). Classification of Rice Leaf Diseases Using Support Vector Machine with HSV and GLCM-Based Feature Extraction. 9(5), 2329–2337.
Seelwal, P., Dhiman, P., Gulzar, Y., Kaur, A., Wadhwa, S., & Onn, C. W. (2024). A systematic review of deep learning applications for rice disease diagnosis : current trends and future directions. September. https://doi.org/10.3389/fcomp.2024.1452961
Sharma, R., Singh, A., Kavita, Jhanjhi, N. Z., Masud, M., Jaha, E. S., & Verma, S. (2021). Plant Disease Diagnosis and Image Classification Using Deep Learning. Computers, Materials and Continua, 71(2), 2125–2140. https://doi.org/https://doi.org/10.32604/cmc.2022.020017
Shrivastava, V. K., & Pradhan, M. K. (2021). Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103(1), 17–26. https://doi.org/10.1007/s42161-020-00683-3
Sovia, N. A., Wayan, N., Wardhani, S., & Sumarminingsih, E. (2025). Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost. 10(1), 278–289.
Talreja, R., Jawrani, V., Watwani, B., Sengupta, S., Rohera, P., & Raghuwanshi, K. (2022). AgriCare: An Android Application for Detection of Paddy Diseases. 1–6. https://doi.org/10.1109/INCET54531.2022.9825038
Tiwari, R., Patel, J., Khan, N. R., & Dadhich, A. (2025). automated identification of rice diseases. 1–16. https://doi.org/10.1371/journal.pone.0307461
Wibisono, N. B., & Saiful, S. (2025). Crop Yield Prediction Using Random Forest Algorithm and Xgboost Machine Learning Model. IX(2454), 1983–1994. https://doi.org/10.47772/IJRISS
Widyawati, W., AR, N. H., Syafrial, S., & Sujarwo, S. (2025). Crafting the future of rice in Indonesia: sustainable supply through systems thinking. Cogent Social Sciences, 11(1), 2488113. https://doi.org/10.1080/23311886.2025.2488113
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