MACHINE LEARNING WITH LIGHTWEIGHT CNN (RESNET-18) FOR EARLY DETECTION OF RICE LEAF DISEASES
DOI:
https://doi.org/10.33480/jitk.v11i4.7358Keywords:
Deep Learning, Image Classification, Plant Disease Detection, ResNet-18, Rice Leaf DiseaseAbstract
Rice leaf diseases such as blast and brown spot significantly threaten rice productivity, especially in agrarian countries like Indonesia. Manual diagnosis methods remain subjective, slow, and inconsistent across field conditions, highlighting the need for an automated and reliable detection system. This study presents a lightweight deep learning framework for the automatic classification of rice leaf diseases from image data. To assess its effectiveness, four Convolutional Neural Network (CNN) architectures ResNet-18, VGG-16, Inception V3, and MobileNetV2 were evaluated. The dataset, obtained from Kaggle, consists of three classes healthy, blast-infected, and brown spot with all images preprocessed through normalization and augmentation before being split into training and validation sets. Experimental results show that ResNet-18 achieves the best overall performance, with 96.94% accuracy, 100% precision, 95.45% recall, an F1-score of 96.18%, and an AUC of 1.0000. Compared to the other architectures, ResNet-18 demonstrates higher stability, stronger generalization, and lower overfitting tendencies while maintaining computational efficiency. The findings indicate that ResNet-18 is a promising lightweight model for practical deployment in mobile or IoT-based agricultural monitoring systems, supporting early disease detection and enhancing local food security efforts
Downloads
References
[1] M. Mavaddat, “Plant Diseases Classification Using Pre-trained and Transfer Learning Models: A Study on Rice Leaves,” Aut Journal of Electrical Engineering, vol. 57, no. 2, pp. 263–282, 2025, doi: 10.22060/eej.2024.23015.5580.
[2] A. R. Khan, “Automatic Diagnosis of Rice Leaves Diseases Using Hybrid Deep Learning Model,” Journal of Advances in Information Technology, vol. 14, no. 3, pp. 418–425, 2023, doi: 10.12720/jait.14.3.418-425.
[3] S. M. Rallapalli, “A contemporary approach for disease identification in rice leaf,” International Journal of Systems Assurance Engineering and Management, 2021, doi: 10.1007/s13198-021-01159-y.
[4] A. Suting, “Rice Leaf Diseases Classification Using Deep Learning Algorithms for Smartphone Application Development: An Empirical Study,” International Journal of Image and Graphics, 2024, doi: 10.1142/S0219467826500300.
[5] P. T. Pavithra, “A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification,” International Journal of Computational Methods and Experimental Measurements, vol. 12, no. 1, pp. 35–43, 2024, doi: 10.18280/ijcmem.120104.
[6] S. Sangeetha, “IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50,” International Journal on Artificial Intelligence Tools, vol. 33, no. 2, 2024, doi: 10.1142/S0218213024500040.
[7] Y. Liu, “Detection method of rice leaf disease based on DeepLabv3—Faster R—CNN,” Journal of Chinese Agricultural Mechanization, vol. 46, no. 4, 2025, doi: 10.13733/j.jcam.issn.2095-5553.2025.04.016.
[8] R. K and M. S. K, “Breast Cancer Prediction by Leveraging Machine Learning and Deep learning Techniques with Different Imaging Modalities,” in 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022, pp. 1–6. doi: 10.1109/I2CT54291.2022.9824749.
[9] I. Ozcan, H. Aydin, and A. Cetinkaya, “Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.,” Asian Pacific journal of cancer prevention : APJCP, vol. 23, no. 10, pp. 3287–3297, Oct. 2022, doi: 10.31557/APJCP.2022.23.10.3287.
[10] A. P. Windarto, T. Herawan, and P. Alkhairi, “Early Detection of Breast Cancer Based on Patient Symptom Data Using Naive Bayes Algorithm on Genomic Data,” in Artificial Intelligence, Data Science and Applications, Y. Farhaoui, A. Hussain, T. Saba, H. Taherdoost, and A. Verma, Eds., Cham: Springer Nature Switzerland, 2024, pp. 478–484.
[11] J. Rashid, S. Batool, J. Kim, M. W. Nisar, and A. Hussain, “An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction,” Front. Public Health, vol. 10, no. March, pp. 1–20, 2022, doi: 10.3389/fpubh.2022.860396.
[12] S. Saeedbakhsh, M. Sattari, M. Mohammadi, J. Najafian, and F. Mohammadi, “Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest.,” Advanced biomedical research, vol. 12, p. 51, 2023, doi: 10.4103/abr.abr_383_21.
[13] A. P. Windarto, I. R. Rahadjeng, M. N. H. Siregar, and P. Alkhairi, “Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 1, pp. 468–476, 2024.
[14] S. Defit, A. P. Windarto, and P. Alkhairi, “Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization,” Telematika, vol. 17, no. 1, pp. 52–67, 2024.
[15] P. Alkhairi, W. Wanayumini, and B. H. Hayadi, “Analysis of the adaptive learning rate and momentum effects on prediction problems in increasing the training time of the backpropagation algorithm,” AIP Conference Proceedings, vol. 3048, no. 1, p. 20049, 2024, doi: 10.1063/5.0203374.
[16] T. A. T. K. Azaharan, “Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection,” International journal of online and biomedical engineering, vol. 19, no. 8, pp. 97–109, 2023, doi: 10.3991/ijoe.v19i08.38619.
[17] W. Al-Khater, “Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware,” Alexandria Engineering Journal, vol. 89, pp. 39–52, 2024, doi: 10.1016/j.aej.2023.12.061.
[18] L. I. Kesuma, “ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 5, pp. 149–161, 2023, doi: 10.22266/ijies2023.1031.14.
[19] F. Edderbali, “Mobilenet, inception ResNet and GoogleNet for epilepsy detection using spectrogram images,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 34, no. 2, pp. 870–877, 2024, doi: 10.11591/ijeecs.v34.i2.pp870-877.
[20] Z. Kaya, “Classification of brain tumors using wavelet transform and Inception V3 convolutional neural network model,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 39, no. 3, pp. 1945–1952, 2024, doi: 10.17341/gazimmfd.1221952.
[21] I. Z. Renu et al., “A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning,” 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024, no. June, pp. 1–6, 2024, doi: 10.1109/ICAEEE62219.2024.10561770.
[22] L. F. de J. Silva, O. A. C. Cortes, and J. O. B. Diniz, “A novel ensemble CNN model for COVID-19 classification in computerized tomography scans,” Results in Control and Optimization, vol. 11, no. September 2022, p. 100215, 2023, doi: 10.1016/j.rico.2023.100215.
[23] T. R. Mahesh, “Early Predictive Model for Detection of Plant Leaf Diseases Using MobileNetV2 Architecture,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 46–54, 2023.
[24] Abhishek, “Classification of Colorectal Cancer using ResNet and EfficientNet Models,” Open Biomedical Engineering Journal, vol. 18, 2024, doi: 10.2174/0118741207280703240111075752.
[25] S. A. El-Feshawy, “IoT framework for brain tumor detection based on optimized modified ResNet 18 (OMRES),” Journal of Supercomputing, vol. 79, no. 1, pp. 1081–1110, 2023, doi: 10.1007/s11227-022-04678-y.
[26] M. Kia, “Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification,” Iran Journal of Computer Science, vol. 8, no. 1, pp. 185–215, 2025, doi: 10.1007/s42044-024-00216-6.
[27] H. A. Shah, F. Saeed, S. Yun, J. H. Park, A. Paul, and J. M. Kang, “A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet,” IEEE Access, vol. 10, pp. 65426–65438, 2022, doi: 10.1109/ACCESS.2022.3184113.
[28] S. Kiziloluk and E. Sert, “COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer,” Medical and Biological Engineering and Computing, vol. 60, no. 6, pp. 1595–1612, 2022, doi: 10.1007/s11517-022-02553-9.
[29] R. Vallabhaneni, “MobileNet based secured compliance through open web application security projects in cloud system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 3, pp. 1661–1669, 2024, doi: 10.11591/ijeecs.v35.i3.pp1661-1669.
[30] C. Mai, “MobileNet-Based IoT Malware Detection with Opcode Features,” Journal of Communications and Information Networks, vol. 8, no. 3, pp. 221–230, 2023, doi: 10.23919/JCIN.2023.10272350.
[31] A. Wahid, “Smart Blended Learning Framework based on Artificial Intelligence using MobileNet Single Shot Detector and Centroid Tracking Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 364–369, 2022, doi: 10.14569/IJACSA.2022.0130543.
[32] R. Chen, “Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet,” IEEE Internet of Things Journal, vol. 10, no. 18, pp. 15966–15979, 2023, doi: 10.1109/JIOT.2023.3268636.
[33] V. Rajasekhar, G. Arulselvi, and K. Suresh Babu, “Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt,” International Journal of Electrical and Computer Engineering, vol. 14, no. 2, pp. 1739–1749, 2024, doi: 10.11591/ijece.v14i2.pp1739-1749.
[34] M. S. H. Shovon, S. J. Mozumder, O. K. Pal, M. F. Mridha, N. Asai, and J. Shin, “PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection,” IEEE Access, vol. 11, no. March, pp. 34846–34859, 2023, doi: 10.1109/ACCESS.2023.3264835.
[35] S. Aboukadri, A. Ouaddah, and A. Mezrioui, “Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Identity and Access Management Field: Challenges and State of the Art BT - Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022,” A. E. Hassanien, V. Snášel, M. Tang, T.-W. Sung, and K.-C. Chang, Eds., Cham: Springer International Publishing, 2023, pp. 50–64.
[36] B. H. Hayadi, I. G. I. Sudipa, and A. P. Windarto, “Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 11–20, 2021, doi: 10.30812/matrik.v21i1.1273.
[37] P. Alkhairi and B. H. Hayadi, “Pemodelan Artificial Neural Network Peramalan Tingkat Kenaikan Jabatan Berdasarkan Kinerja Pegawai Menggunakan Backpropagation,” Smart EDU: Buletin Education, vol. 1, no. 2, pp. 73–84, 2022.
[38] Z. Wang, “CNN- and GAN-based classification of malicious code families: A code visualization approach,” International Journal of Intelligent Systems, vol. 37, no. 12, pp. 12472–12489, 2022, doi: 10.1002/int.23094.
[39] Y. Zhu, H. Sekiya, T. Okatani, M. Tai, and S. Morichika, “B-CNN: a deep learning method for accelerometer-based fatigue cracks monitoring system,” Journal of Civil Structural Health Monitoring, vol. 13, no. 4–5, pp. 947–959, Jun. 2023, doi: 10.1007/s13349-023-00690-9.
[40] T. H. Chun, “Efficacy of the Image Augmentation Method using CNN Transfer Learning in Identification of Timber Defect,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 107–114, 2022, doi: 10.14569/IJACSA.2022.0130514.
[41] P. Pankaj, “A New design of occlusion Invariant Face Recognition Using optimal Pattern extraction and CNN with GRU-Based Architecture,” International Journal of Information Security and Privacy, vol. 16, no. 1, 2022, doi: 10.4018/IJISP.305222.
[42] D. Dablain, B. Krawczyk, and N. V. Chawla, “DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 9, pp. 6390–6404, 2023, doi: 10.1109/TNNLS.2021.3136503.
[43] K. Punithavathi, “Enhanced Breast Cancer Detection and Classification Using CNN-EfficientNet B3: A Deep Learning Approach with Segmentation and Feature Extraction from MIAS Dataset,” SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 1, pp. 257–270, 2025, doi: 10.14445/23488549/IJECE-V12I1P120.
[44] X. Wang, “OsACA9, an Autoinhibited Ca2+-ATPase, Synergically Regulates Disease Resistance and Leaf Senescence in Rice,” International Journal of Molecular Sciences, vol. 25, no. 3, 2024, doi: 10.3390/ijms25031874.
[45] D. Chaudhari, “Remora-CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification,” Journal of Phytopathology, vol. 172, no. 5, 2024, doi: 10.1111/jph.13411.
[46] N. D. Luu, “Application of deep learning for rice leaf disease detection in the Mekong Delta,” Ctu Journal of Innovation and Sustainable Development, vol. 16, pp. 1–7, 2024, doi: 10.22144/ctujoisd.2024.316.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Poningsih, Suhendra, Ahmad Zamsuri
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
