Teknik Pembesaran Citra Progresif untuk Klasifikasi Tipe Penyakit Daun Padi pada Jaringan Syaraf Tiruan VGG16

Bayu Adhi Nugroho

Abstract


Tanaman padi merupakan bahan pangan utama di Indonesia. Produksi tanaman padi sangat bergantung pada hasil panen. Maka, sebuah upaya untuk mendukung kuantitas jumlah panen sangat penting. Penyakit – penyakit daun padi telah diketahui dapat menurunkan kuantitas produksi panen. Teknologi kecerdasan buatan untuk melakukan klasifikasi penyakit – penyakit daun padi sangat membantu manusia karena teknologi semacam ini tidak mengenal lelah dan dapat melakukan klasifikasi data dalam jumlah besar dengan cepat. Metode klasifikasi tradisional seperti SVM atau Naive Bayes dapat digunakan, tetapi metode-metode semacam ini memerlukan data pre-processing yang kompleks sebelum dapat digunakan dalam metode tersebut. Metode berbasis Jaringan Syaraf Tiruan (misal: Deep Learning) memiliki keunggulan lebih mudah digunakan dengan data berupa citra. Riset ini menggunakan teknik pembesaran citra progresif dengan arsitektur VGG16. Hasil akhir dari eksperimen yang dilakukan sangat signifikan dengan tingkat akurasi sebesar 96,62 %

Keywords


cnn; klasifikasi; citra; penyakit daun padi

Full Text:

PDF

References


M. I. Vadilaksono, Y. Syaukat, and W. Widyastutik, “Harmonization of Rice Production Policy and Rice Trade Policy in Indonesia,” J. Manaj. Dan Agribisnis, Mar. 2023, doi: 10.17358/jma.20.1.154.

Noviyanto Rahmadi, “Harga Beras Terus Melonjak Naik di 268 Kabupaten/Kota.” [Online]. Available: https://ppid.samarindakota.go.id/berita/kabar-pemerintahan/harga-beras-terus-melonjak-naik-di-268-kabupatenkota

C. H. Bock, K.-S. Chiang, and E. M. Del Ponte, “Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy,” Trop. Plant Pathol., vol. 47, no. 1, pp. 25–42, Feb. 2022, doi: 10.1007/s40858-021-00439-z.

Z. LIU, J. HUANG, and R. TAO, “Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data,” Rice Sci., vol. 15, no. 3, pp. 232–242, 2008, doi: https://doi.org/10.1016/S1672-6308(08)60047-5.

B. S. Bari et al., “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” PeerJ Comput. Sci., vol. 7, p. e432, Apr. 2021, doi: 10.7717/peerj-cs.432.

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, p. 105527, 2020, doi: https://doi.org/10.1016/j.compag.2020.105527.

Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, and B. Yang, “Application of Support Vector Machine for Detecting Rice Diseases Using Shape and Color Texture Features,” in 2009 International Conference on Engineering Computation, 2009, pp. 79–83. doi: 10.1109/ICEC.2009.73.

W. Liang, H. Zhang, G. Zhang, and H. Cao, “Rice Blast Disease Recognition Using a Deep Convolutional Neural Network,” Sci. Rep., vol. 9, no. 1, p. 2869, Feb. 2019, doi: 10.1038/s41598-019-38966-0.

M. Gogoi and S. A. Begum, “Progressive 3-Layered Block Architecture for Image Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 3, 2022, doi: 10.14569/IJACSA.2022.0130360.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.

M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning, K. Chaudhuri and R. Salakhutdinov, Eds., in Proceedings of Machine Learning Research, vol. 97. PMLR, Jun. 2019, pp. 6105–6114. [Online]. Available: https://proceedings.mlr.press/v97/tan19a.html

D. Jerrish et al., “Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models,” Multimed. Tools Appl., vol. 83, pp. 1–38, Aug. 2023, doi: 10.1007/s11042-023-16306-9.

P. K. Sethy, “Rice Leaf Disease Image Samples.” Mendeley, 2020. doi: 10.17632/FWCJ7STB8R.1.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09, 2009.

L. Wright, “Ranger-Deep-Learning-Optimizer.” Github, 2019. [Online]. Available: https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer




DOI: https://doi.org/10.31284/p.snestik.2024.5845

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 bayu adhi nugroho

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.