Evaluasi Komparatif Lightweight Convolutional Neural Network Untuk Klasifikasi Penyakit Daun dan Hama Tanaman Padi
-
Afis Julianto
*
Politeknik Negeri Bengkalis, Bengkalis,
Indonesia
- Miftahul Jannah Politeknik Negeri Bengkalis, Bengkalis, Indonesia (*) Corresponding Author
Abstract
Rice is a critical commodity for national food security; however, its productivity is frequently reduced due to leaf diseases and pests. Conventional identification methods that rely on visual observation are often inefficient and prone to subjectivity, particularly given the complex and variable nature of symptoms. This study to evaluate and compare the performance of several lightweight CNN architectures in accurately and efficiently detecting rice leaf diseases and pests on resource constrained devices. This study compares four CNN lightweight architectures: MobileNetV2, EfficientNetV2-B3, NasNetMobile, and a custom CNN Lightweight Architecture, all using a 13-class dataset that underwent preprocessing, augmentation, and data balancing. The models were trained for 100 epochs using the Adam optimizer. Experimental results show that EfficientNetV2B3 achieved the best performance, with 97% accuracy, precision, recall, and F1-score, followed by MobileNetV2 and NasNetMobile, which achieved 94% accuracy. The Custom CNN lightweight model produced 91% accuracy with a model size of only 0.53 MB. Overall, this study provides recommendations for developing accurate and efficient lightweight CNN models to support rice disease and pest detection on mobile devices, IoT systems, and edge computing platforms.
Downloads
References
A. U. Astagina, E. Juniar, S. Mutmainah, and T. A. Lorosae, “Klasifikasi Hama dan Penyakit Tanaman Padi Menggunakan Algoritma Decision Tree,” Journal of Computer Science and Informatics, vol. 2, no. 2, 2025, doi: 10.34304/scientific.v2i2.376.
Y. Istikorini, S. N. Rohmah, A. Y. Maulina, M. Fiqri, M. R. Fortunata, V. K. A. S. Sutanto, D. P. Ardian, F. Z. Navizan, and I. F. Alam, “Penyuluhan Hama dan Penyakit pada Tanaman Padi dan Hortikultura di Desa Cihamerang, Sukabumi,” Jurnal Pusat Inovasi Masyarakat, vol. 7, no. 1, 2025, doi: 10.29244/jpim.7.1.102-115.
N. Habibah, R. A. M. Ramadhan, N. H. Emila, J. Sani, and N. Wulandari, “Inventarisasi Hama Penyakit Tanaman Padi di Desa Sukaharja Kecamatan Cisayong Kabupaten Tasikmalaya,” Jurnal Pertanian Cemara, vol. 21, no. 1, 2024, doi: 10.24929/fp.v21i1.3420.
Wagiyanti, H. Hamidson, and Suwandi, “Intensity and Incidence of Pest Disease Attacks on Rice Plants in Enggal Rejo Village, Air Salek Subdistrict,” Journal of Global Sustainable Agriculture, vol. 4, no. 2, 2024, doi: 10.32502/jgsa.v4i2.8408.
M. S. Pramono and A. P. Wibowo, “Penerapan Convolutional Neural Network untuk Identifikasi Penyakit pada Tanaman Padi dari Citra Daun Menggunakan ResNet-101,” Djtechno: Jurnal Teknologi Informasi, vol. 5, no. 3, 2024, doi: 10.46576/djtechno.v5i3.5098.
R. R. Burhanuddin, “Klasifikasi Penyakit Padi Melalui Citra Daun Menggunakan Metode Naive Bayes,” JITET: Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, 2024, doi: 10.23960/jitet.v12i2.4012.
H. Terzioğlu, A. Gölcük, A. M. A. Shakarji, and M. Y. Al-Bayati, “Comparative Analysis of Deep Learning-Based Feature Extraction and Traditional Classification Approaches for Tomato Disease Detection,” Agronomy, vol. 15, no. 7, 2025, doi: 10.3390/agronomy15071509.
W. Jia, J. Gao, W. Xia, Y. Zhao, H. Min, and J.-T. Lu, “A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition,” International Journal of Automation and Computing, vol. 18, no. 1, 2021, doi: 10.1007/s11633-020-1257-9.
R. Kusuma and R. Rajkumar, “Plant Leaf Disease Detection and Classification Using Artificial Intelligence Techniques: A Review,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 38, no. 2, 2025, doi: 10.11591/ijeecs.v38.i2.pp1308-1323.
S. Z. M. Zaki, M. A. Zulkifley, M. M. Stofa, N. A. M. Kamari, and N. A. Mohamed, “Classification of Tomato Leaf Diseases Using MobileNet V2,” IAES International Journal of Artificial Intelligence, vol. 9, no. 2, 2020, doi: 10.11591/ijai.v9.i2.pp290-296.
A. Julianto and A. Sunyoto, “A Performance Evaluation of Convolutional Neural Network Architecture for Classification of Rice Leaf Disease,” IAES International Journal of Artificial Intelligence, vol. 10, no. 4, 2021, doi: 10.11591/ijai.v10.i4.pp1069-1078.
U. Suttapakti and A. Bunpeng, “Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction,” in Proceedings of the International Symposium on Communications and Information Technologies (ISCIT), 2019, doi: 10.1109/ISCIT.2019.8905128
P. Deepika and B. Arthi, “Prediction of Plant Pest Detection Using Improved Mask-FRCNN in Cloud Environment,” Measurement: Sensors, vol. 24, 2022, doi: 10.1016/j.measen.2022.100549.
S. Liu, J. Zhao, H. Wang, R. Li, and Z. Lu, “Using Convolutional Neural Networks to Realize Deep Recognition Analysis of Power Technology Standard Images,” Procedia Computer Science, vol. 243, 2024, doi: 10.1016/j.procs.2024.09.040.
M. F. A. Maulana, N. M. Anggadimas, and D. A. Sani, “Klasifikasi Citra Penyakit Daun Padi dengan Metode CNN Menggunakan Arsitektur ResNet50V2,” CESS: Journal of Computer Engineering, System and Science, vol. 10, no. 2, 2025, doi: 10.24114/cess.v10i2.66960.
P. I. Ritharson, K. Raimond, X. A. Mary, J. E. Robert, and A. J., “DeepRice: A Deep Learning and Deep Feature-Based Classification of Rice Leaf Disease Subtypes,” Artificial Intelligence in Agriculture, vol. 11, 2024, doi: 10.1016/j.aiia.2023.11.001.
D. Ngo, H. C. Park, and B. Kang, “Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments,” Electronics, vol. 14, no. 12, 2025, doi: 10.3390/electronics14122495.
Y. A. Sitorus et al., “Evaluasi Komparatif Arsitektur Lightweight CNN, MobileNetV2, dan EfficientNetB0 dalam Deteksi Penyakit Daun Jagung,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 9, no. 8, 2025.
S. Yuliany, Aradea, and A. N. Rachman, “Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network,” Jurnal Buana Informatika, vol. 13, no. 1, 2022, doi: 10.24002/jbi.v13i1.5022.
C. Pal, S. Karmakar, I. Mukherjee, and P. P. Chakrabarti, “A Lightweight and Explainable CNN Model for Empowering Plant Disease Diagnosis,” Scientific Reports, vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-94083-1.
A. Julianto, A. Sunyoto, and F. W. Wibowo, “Optimasi Hyperparameter Convolutional Neural Network untuk Klasifikasi Penyakit Tanaman Padi,” TEKNIMEDIA: Teknologi Informasi dan Multimedia, vol. 3, no. 2, 2022, doi: 10.46764/teknimedia.v3i2.77.
L. D. Quach, Q. K. Nguyen, Q. A. Nguyen, and L. T. T. Lan, “Rice Pest Dataset Supports the Construction of Smart Farming Systems,” Data in Brief, vol. 52, 2024, doi: 10.1016/j.dib.2024.110046.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, doi: 10.1109/CVPR.2018.00474.
H. A. Mubarak and R. Novita, “Klasifikasi Citra X-Ray Tuberkulosis Menggunakan Convolutional Neural Networks,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6515.
M. Tan and Q. V. Le, “EfficientNetV2: Smaller Models and Faster Training,” Proceedings of Machine Learning Research, vol. 139, 2021, doi: 10.48550/arXiv.2104.00298.
S. Ibrahim, H. A. Abdallah, K. M. Amin, R. I. Alkanhel, and M. Ibrahim, “Soft Attention-Based EfficientNetV2B3 Model for Skin Cancer Disease Classification Using Dermoscopy Images,” IEEE Access, vol. 12, 2024, doi: 10.1109/ACCESS.2024.3486153.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, doi: 10.1109/CVPR.2018.00907.
T. S. Winanto, C. Rozikin, and A. Jamaludin, “Analisa Performa Arsitektur Transfer Learning untuk Mengidentifikasi Penyakit Daun pada Tanaman Pangan,” Journal of Applied Informatics and Computing, vol. 7, no. 1, 2023, doi: 10.30871/jaic.v7i1.5991.
C. Manliguez, “Generalized Confusion Matrix for Multiple Classes,” pp. 5–7, 2016, doi: 10.13140/RG.2.2.31150.51523.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference for Learning Representations, 2014, doi: 10.48550/arXiv.1412.6980.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Evaluasi Komparatif Lightweight Convolutional Neural Network Untuk Klasifikasi Penyakit Daun dan Hama Tanaman Padi
Pages: 1844-1853
Copyright (c) 2025 Afis Julianto, Miftahul Jannah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
