Penggunaan Transfer Learning untuk Peningkatan Akurasi Deteksi Penyakit Tanaman Bunga
DOI:
https://doi.org/10.61132/uranus.v3i4.1292Keywords:
Computer Vision, Deep Learning, Flowering Plants, Plant Disease Detection, Transfer LearningAbstract
Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.
References
Alit Farisi Oria, Alit FarisiTri Handoyo, Distiana Wulanjari, dan Susan Barbara Patricia. 2019. Introduksi Mawar Potong Untuk Meningkatkan Pendapatan Petani Mawar Di Desa Karangpring Kecamatan Sukorambi Kabupaten Jember.Jurnal Seminar Nasional, 1-4.
Alam, T. S., Jowthi, C. B. and Pathak, A. (2024) ‘Comparing pre-trained models for
efficient leaf disease detection: a study on custom CNN’, Journal of Electrical Systems and Information Technology, 11(1), pp. 1–26. doi: 10.1186/s43067-024-00137-1.
Boulent, J., et al. (2020). Convolutional neural networks in plant phenotyping and disease detection. Computers and Electronics in Agriculture.
Dehbozorgi, P., Ryabchykov, O. and Bocklitz, T. (2024) ‘A Systematic Investigation of
Image Pre-Processing on Image Classification’, IEEE Access, 12(February), pp. 64913–64926. doi: 10.1109/ACCESS.2024.3395063.
Kumar, T. et al. (2023) ‘Image Data Augmentation Approaches: A Comprehensive Survey and Future directions’, 12(December). doi: 10.1109/ACCESS.2024.3470122.
Santoso, H. A. et al. (2024) ‘Comparative analysis of convolutional neural network and
DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification’,Applied Computing and Informatics. doi: 10.1108/ACI-03-2024-0132.
Swapno, S. M. M. R. et al. (2025) ‘ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruitsclassification’, Neural Computing and Applications, 5, pp. 7–14. doi: 10.1007/s00521-025-10973-5.
Tan, M., & Le, Q. (2020). EfficientNet: Rethinking model scaling for convolutional neural networks. Journal of Machine Learning Research.
Liu, H. et al. (2022) ‘TBEM: Testing-Based GPU-Memory Consumption Estimation for
Deep Learning’, IEEE Access, 10, pp. 39674–39680. doi: 10.1109/ACCESS.2022.3164510.
Lanjewar, M. G., Morajkar, P. and P, P. (2024) ‘Modified transfer learning frameworks
to identify potato leaf diseases’, Multimedia Tools and Applications, 83(17), pp. 50401–50423. doi: 10.1007/s11042-023-17610-0.
Wu, Q. et al. (2023) ‘A classification method for soybean leaf diseases based on an
improved ConvNeXt model’, Scientific Reports, 13(1), pp. 1–11. doi: 10.1038/s41598-023-46492-3.
Zhuang, H. et al. (2024) ‘An Attention-Based Deep Learning Network for Predicting
Platinum Resistance in Ovarian Cancer’, IEEE Access, 12(March), pp. 41000–41008. doi:10.1109/ACCESS.2024.3377560.
Jaiswal, A., et al. (2020). A survey on deep learning applications in agriculture. International Journal of Advanced Computer Science.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
