Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101

Classification of Rice Diseases based on Leaf Image Using Resnet101 Trained Model

  • Ulfah Nur Oktaviana Universitas Muhammadiyah Malang
  • Ricky Hendrawan Universitas Muhammadiyah Malang
  • Alfian Dwi Khoirul Annas Universitas Muhammadiyah Malang
  • Galih Wasis Wicaksono Universitas Muhammadiyah Malang https://orcid.org/0000-0002-8096-1762
Keywords: Classification, Disease, Paddy, Image, CNN, ResNet101

Abstract

Rice is a staple food source for most countries in the world, including Indonesia. The problem of rice disease is a problem that is quite crucial and is experienced by many farmers. Approximately 200,000 - 300,000 tons per year the amount of rice attacked by pests in Indonesia. Considerable losses are caused by late-diagnosed rice plant diseases that reach a severe stage and cause crop failure. The limited number of Agricultural Extension Officers (PPL) and the Lack of information about disease and proper treatment are some of the causes of delays in handling rice diseases. Therefore, with the development of information technology and computers, it is possible to identify diseases by utilizing Artificial Intelligence, one of which is by using recognition methods based on image processing and pattern recognition technology. The purpose of this research is to create a Machine Learning model by applying the model architecture from Resnet101 combined with the model architecture from the author. The model proposed in this study produces an accuracy of 98.68%.

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References

E. Anggiratih, S. Siswanti, S. K. Octaviani, and A. Sari, “Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning,” J. Ilm. SINUS, vol. 19, no. 1, p. 75, 2021, doi: 10.30646/sinus.v19i1.526.

A. Purnamawati, W. Nugroho, D. Putri, and W. Hidayat, “Deteksi Penyakit Daun Pada Tanaman Padi Menggunakan Algoritma Decision Tree , Random Forest , Naïve Bayes , Svm Dan Knn,” Info Tekjar J. Nas. Inform. dan Teknol. Jar., vol. 5, no. 1, pp. 212–215, 2020.

J. Kusanti, K. Penyakit, D. Padi, and A. Haris, “Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut,” J. Inform. J. Pengemb. IT, vol. 03, no. 01, pp. 1–6, 2018.

BPS, “Prediksi Padi Menurut Kabupaten Kota di Jawa Timur,” Badan Pusat Statistik Provinsi Jawa Timur, 2018. .

R. Saptono and A. Doewes, “Deteksi dini hama dan penyakit tanaman padi memanfaatkan masukan tekstual dengan metode cosine similarity,” Semin. Ilm. Ilmu Komput., no. September 2018, pp. 1–16, 2014.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.

M. Z. Alom et al., “A state-of-the-art survey on deep learning theory and architectures,” Electron., vol. 8, no. 3, 2019, doi: 10.3390/electronics8030292.

W. jie Liang, H. Zhang, G. feng Zhang, and H. xin Cao, “Rice Blast Disease Recognition Using a Deep Convolutional Neural Network,” Sci. Rep., vol. 9, no. 1, pp. 1–10, 2019, doi: 10.1038/s41598-019-38966-0.

C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst. Eng., vol. 194, pp. 112–120, 2020, doi: 10.1016/j.biosystemseng.2020.03.020.

M. A. Islam, N. Rahman Shuvo, M. Shamsojjaman, S. Hasan, S. Hossain, and T. Khatun, “An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 1, pp. 280–288, 2021, doi: 10.14569/IJACSA.2021.0120134.

J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, no. November 2019, p. 105393, 2020, doi: 10.1016/j.compag.2020.105393.

H. B. Prajapati, J. P. Shah, and V. K. Dabhi, “Detection and classification of rice plant diseases,” Intell. Decis. Technol., vol. 11, no. 3, pp. 357–373, 2017, doi: 10.3233/IDT-170301.

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, vol. 58, pp. 1–5, doi: 10.1109/STI47673.2019.9068096.

K. Thenmozhi and U. Srinivasulu Reddy, “Crop pest classification based on deep convolutional neural network and transfer learning,” Comput. Electron. Agric., vol. 164, no. June, p. 104906, 2019, doi: 10.1016/j.compag.2019.104906.

M. K. Priya and S. Dhanabal, “Analyses of Nine Different Types of Diseases in Paddy with Hybrid Algorithms using Deep Learning,” vol. 8, no. 08, pp. 1–7, 2020.

R. Jain, P. Nagrath, G. Kataria, V. Sirish Kaushik, and D. Jude Hemanth, “Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning,” Meas. J. Int. Meas. Confed., vol. 165, p. 108046, 2020, doi: 10.1016/j.measurement.2020.108046.

A. Chakraborty, D. Kumer, and K. Deeba, “Plant Leaf Disease Recognition Using Fastai Image Classification,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. Iccmc, pp. 1624–1630, 2021, doi: 10.1109/ICCMC51019.2021.9418042.

L. W. Liu, S. H. Hsieh, S. J. Lin, Y. M. Wang, and W. S. Lin, “Rice blast (Magnaporthe oryzae) occurrence prediction and the key factor sensitivity analysis by machine learning,” Agronomy, vol. 11, no. 4, pp. 1–15, 2021, doi: 10.3390/agronomy11040771.

M. Koklu, I. Cinar, and Y. S. Taspinar, “Classification of rice varieties with deep learning methods,” Comput. Electron. Agric., vol. 187, no. June, p. 106285, 2021, doi: 10.1016/j.compag.2021.106285.

S. Ramesh and D. Vydeki, “Application of machine learning in detection of blast disease in south indian rice crops,” J. Phytol., vol. 11, pp. 31–37, 2019, doi: 10.25081/jp.2019.v11.5476.

A. Sagar and J. Dheeba, “On using transfer learning for plant disease detection,” bioRxiv, 2020, doi: 10.1101/2020.05.22.110957.

Published
2021-12-31
How to Cite
Ulfah Nur Oktaviana, Ricky Hendrawan, Alfian Dwi Khoirul Annas, & Galih Wasis Wicaksono. (2021). Klasifikasi Penyakit Padi berdasarkan Citra Daun Menggunakan Model Terlatih Resnet101. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1216 - 1222. https://doi.org/10.29207/resti.v5i6.3607
Section
Artikel Teknologi Informasi