• Ahmad Rofiqul Muslikh Faculty of Information Technology, Universitas Merdeka, Malang, East Java, Indonesia
  • De Rosal Ignatius Moses Setiadi Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Central Java, Indonesia
  • Arnold Adimabua Ojugo Department of Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria
Keywords: Convolutional Neural Network, Image recognition, Paddy disease identification, Transfer Learning, Xception pre-trained model


As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.


Download data is not yet available.


Humas BRIN, “Peneliti BRIN Bahas Penyakit Blas (Pyricularia oryzae) pada Tanaman Padi,” BRIN-Badan Riset dan Inovasi Nasional, 2023. https://brin.go.id/news/113303/peneliti-brin-bahas-penyakit-blas-pyricularia-oryzae-pada-tanaman-padi (accessed Nov. 15, 2023).

V. K. Shrivastava, M. K. Pradhan, S. Minz, and M. P. Thakur, “Rice plant disease classification using transfer learning of deep convolution neural network,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 3/W6, pp. 631–635, 2019, doi: 10.5194/isprs-archives-XLII-3-W6-631-2019.

D. J. Chaudhari and K. Malathi, “Detection and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model,” Opt. Mem. Neural Networks (Information Opt., vol. 32, no. 1, pp. 39–57, 2023, doi: 10.3103/S1060992X2301006X.

R. A. Saputra, Suharyanto, S. Wasiyanti, D. F. Saefudin, A. Supriyatna, and A. Wibowo, “Rice Leaf Disease Image Classifications Using KNN Based On GLCM Feature Extraction,” J. Phys. Conf. Ser., vol. 1641, no. 1, p. 012080, Nov. 2020, doi: 10.1088/1742-6596/1641/1/012080.

S. Ramesh, V. Mohanavel, S. Diwakaran, M. U, and A. G, “Detection of critical diseases in rice crop Using GLCM Texture Feature Algorithm,” in 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Jan. 2022, pp. 1–5. doi: 10.1109/ACCAI53970.2022.9752483.

V. Satgunalingam and R. Thaneeshan, “Automatic Paddy Leaf Disease Detection Based on GLCM Using Multiclass Support Vector Machine,” Int. J. Comput., vol. 39, no. 1, pp. 97–106, 2020, [Online]. Available: https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1841

C. Imam, E. W. Hidayat, and N. I. Kurniati, “Classification of Meat Imagery Using Artificial Neural Network Method and Texture Feature Extraction By Gray Level Co-Occurrence Matrix Method,” J. Tek. Inform., vol. 2, no. 1, pp. 1–8, 2021, doi: 10.20884/1.jutif.2021.2.1.37.

K. Ahmed, T. R. Shahidi, S. M. Irfanul Alam, and S. Momen, “Rice Leaf Disease Detection Using Machine Learning Techniques,” in 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dec. 2019, pp. 1–5. doi: 10.1109/STI47673.2019.9068096.

J. W. Orillo, J. Dela Cruz, L. Agapito, P. J. Satimbre, and I. Valenzuela, “Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network,” in 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Nov. 2014, pp. 1–6. doi: 10.1109/HNICEM.2014.7016248.

M. A. Araaf and K. Nugroho, “Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm,” J. Comput. Theor. Appl., vol. 1, no. 1, pp. 31–40, Sep. 2023, doi: 10.33633/jcta.v1i1.9185.

M. Yogeshwari and G. Thailambal, “Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks,” Mater. Today Proc., vol. 81, no. 2, pp. 530–536, 2021, doi: 10.1016/j.matpr.2021.03.700.

M. S. Sunarjo and H. Gan, “High-Performance Convolutional Neural Network Model to Identify COVID-19 in Medical Images,” J. Comput. Theor. Appl., vol. 1, no. 1, pp. 19–30, Aug. 2023, doi: 10.33633/jcta.v1i1.8936.

S. B. Imanulloh and A. R. Muslikh, “Plant Diseases Classification based Leaves Image using Convolutional Neural Network,” J. Comput. Theor. Appl., vol. 1, no. 1, pp. 1–10, Aug. 2023, doi: 10.33633/jcta.v1i1.8877.

Y. N. Fuadah, S. Saidah, N. K. Sy, R. Magdalena, and I. Da’wan Ubaidullah, “Glaucoma Classification Based on Fundus Images Processing with Convolutional Neural Network,” J. Tek. Inform., vol. 3, no. 3, p. 719, 2022, [Online]. Available: http://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/276

R. F. Alya, M. Wibowo, and P. Paradise, “Classification of Batik Motif Using Transfer Learning on Convolutional Neural Network (Cnn),” J. Tek. Inform., vol. 4, no. 1, pp. 161–170, 2023, doi: 10.52436/1.jutif.2023.4.1.564.

K. Kamaludin, W. I. Rahayu, M. Yusril, and H. Setywan, “Transfer Learning to Predict Genre Based on Anime Posters,” J. Tek. Inform., vol. 4, no. 5, pp. 1041–1052, 2023, doi: 10.52436/1.jutif.2023.4.5.860.

P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Deep feature based rice leaf disease identification using support vector machine,” Comput. Electron. Agric., vol. 175, no. May, p. 105527, 2020, doi: 10.1016/j.compag.2020.105527.

J. Yang, Y. Xu, H. Cao, H. Zou, and L. Xie, “Deep learning and transfer learning for device-free human activity recognition: A survey,” J. Autom. Intell., vol. 1, no. 1, p. 100007, 2022, doi: 10.1016/j.jai.2022.100007.

X. Yu, J. Wang, Q. Q. Hong, R. Teku, S. H. Wang, and Y. D. Zhang, “Transfer learning for medical images analyses: A survey,” Neurocomputing, vol. 489, pp. 230–254, 2022, doi: 10.1016/j.neucom.2021.08.159.

S. E. M. A. ATASEVER, N. U. H. AZGINOGLU, D. S. TERZI, and R. TERZI, “A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning,” Clin. Imaging, vol. 94, no. November 2022, pp. 18–41, 2023, doi: 10.1016/j.clinimag.2022.11.003.

S. Ghosal and K. Sarkar, “Rice Leaf Diseases Classification Using CNN With Transfer Learning,” in 2020 IEEE Calcutta Conference (CALCON), Feb. 2020, pp. 230–236. doi: 10.1109/CALCON49167.2020.9106423.

J. Chen, D. Zhang, Y. A. Nanehkaran, and D. Li, “Detection of rice plant diseases based on deep transfer learning,” J. Sci. Food Agric., vol. 100, no. 7, pp. 3246–3256, May 2020, doi: 10.1002/jsfa.10365.

K. N, L. V. Narasimha Prasad, C. S. Pavan Kumar, B. Subedi, H. B. Abraha, and V. E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning,” Environ. Res., vol. 198, no. May, p. 111275, 2021, doi: 10.1016/j.envres.2021.111275.

F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, vol. 7, no. 3, pp. 1800–1807. doi: 10.1109/CVPR.2017.195.

X. Wu, R. Liu, H. Yang, and Z. Chen, “An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning,” in 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Dec. 2020, pp. 262–267. doi: 10.1109/ITCA52113.2020.00063.

M. A. Moid and M. Ajay Chaurasia, “Transfer Learning-based Plant Disease Detection and Diagnosis System using Xception,” in 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Nov. 2021, pp. 1–5. doi: 10.1109/I-SMAC52330.2021.9640694.

Rismiyati, S. N. Endah, Khadijah, and I. N. Shiddiq, “Xception Architecture Transfer Learning for Garbage Classification,” in 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), Nov. 2020, pp. 1–4. doi: 10.1109/ICICoS51170.2020.9299017.

F. Mustofa, A. N. Safriandono, and A. R. Muslikh, “Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest,” J. Comput. Theor. Appl., vol. 1, no. 1, pp. 41–48, Sep. 2023, doi: 10.33633/jcta.v1i1.9190.

How to Cite
A. R. Muslikh, D. R. I. M. Setiadi, and A. A. Ojugo, “RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK”, J. Tek. Inform. (JUTIF), vol. 4, no. 6, pp. 1535-1540, Dec. 2023.