Detection of Diseases in Rice Leaves Based on Image Processing Using the Convolutional Neural Network (CNN) Method

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Published Apr 13, 2023
https://doi.org/10.32722/multinetics.v9i1.5255
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Vol. 9 No. 1 (2023): MULTINETICS Mei (2023)

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Syenira Sheila
Universitas Negeri Jakarta
Irma Permata Sari
Universitas Negeri Jakarta
Adrie Bagas Saputra
Universitas Negeri Jakarta
Muhammad Kharil Anwar
Universitas Negeri Jakarta
Farid Restu Pujianto
Universitas Negeri Jakarta

Abstract

Abstract - Rice, produced by rice plants, is a staple food for the people of Asia, especially Indonesia. The amount of rice consumption is increasing along with the increase in population. Therefore, it is essential to keep rice production stable so that demand can be fulfilled. Leaf disease of rice plants is the biggest cause of crop failure which can lead to production instability. This study aims to detect disease in rice plant leaves based on their type using the Convolutional Neural Network (CNN) with the VGG16 architecture base model. In this study, a dataset of rice plant leaf images was collected using the secondary data collection method. The dataset was obtained from the Kaggle dataset repository in .csv format published by Vbookshelf. The image dataset consists of 120 data which is divided into 3 classes based on the type of rice leaf disease namely bacterial leaf bright, brown spot, and leaf smut with each class consisting of 40 image data. In this study, the resizing process was carried out on image data with a size of 224x224 pixels and image data augmentation. The training and testing results in this study showed that the training accuracy value reached 0.9861 and the validation accuracy value reached 1.0. By using the accuracy value in the Confusion Matrix which is equal to 100%, it is obtained that the results of applying the Convolutional Neural Network (CNN) have perfect accuracy.

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Creative Commons License

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

How to Cite
Sheila, S., Permata Sari, I., Bagas Saputra, A. ., Kharil Anwar, M., & Restu Pujianto, F. (2023). Detection of Diseases in Rice Leaves Based on Image Processing Using the Convolutional Neural Network (CNN) Method. MULTINETICS , 9(1), 27–34. https://doi.org/10.32722/multinetics.v9i1.5255

References

  1. Badan Pusat Statistik, “Badan Pusat Statistik.” pp. 335–58, 2017, doi: 10.1055/s-2008-1040325.
  2. B. Nuryanto, “Pengendalian Penyakit Tanaman Padi Berwawasan Lingkungan Melalui Pengelolaan Komponen Epidemik,” J. Penelit. dan Pengemb. Pertan., vol. 37, no. 1, p. 1, 2018, doi: 10.21082/jp3.v37n1.2018.p1-8.
  3. N. A. Haris, “Kombinasi Ciri Bentuk dan Ciri Tekstur Untuk Identifikasi Penyakit Pada Tanaman Padi,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 237–250, 2020, doi: 10.35957/jatisi.v7i2.239.
  4. A. J. Rozaqi, A. Sunyoto, and M. rudyanto Arief, “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network,” Creat. Inf. Technol. J., vol. 8, no. 1, p. 22, 2021, doi: 10.24076/citec.2021v8i1.263.
  5. Fitrianingsih and Rodiah, “Klasifikasi Jenis Citra Daun Mangga Menggunakan Convolutional Neural Network,” J. Ilm. Teknol. dan Rekayasa, vol. 25, no. 3, pp. 223–238, 2020, doi: 10.35760/tr.2020.v25i3.3519.
  6. N. Khasawneh, E. Faouri, and M. Fraiwan, “Automatic Detection of Tomato Diseases Using Deep Transfer Learning,” Appl. Sci., vol. 12, no. 17, 2022, doi: 10.3390/app12178467.
  7. A. Hidayat, U. Darusalam, I. Technology, and S. Jakarta, “DETECTION OF DISEASE ON CORN PLANTS USING CONVOLUTIONAL NEURAL NETWORK METHODS,” vol. 1, pp. 51–56, 2019.
  8. S. Morris, “Image classification using SVM,” IEEE Xplore, pp. 1–10, 2018, [Online]. Available: https://rpubs.com/Sharon_1684/454441.
  9. S. P. Titus Rappan, “Mencegah Dan Mengendalikan Penyakit Blas Pada Tanaman Padi,” 2019. http://cybex.pertanian.go.id/mobile/artikel/72607/Mencegah-Dan-Mengendalikan-Penyakit-Blas-Pada-Tanaman-Padi/ (accessed Mar. 18, 2023).
  10. “Penyakit Kresek_Hawar Daun (Bacterial Leaf Blight) - Kabupaten Bogor.” .
  11. I. S. Mustika, “TUNGRO SALAH SATU PENYAKIT PADA TANAMAN PADI SAWAH,” 2019. http://cybex.pertanian.go.id/mobile/artikel/88709/TUNGRO-SALAH-SATU-PENYAKIT-PADA-TANAMAN-PADI-SAWAH/ (accessed Mar. 18, 2023).
  12. L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017, [Online]. Available: http://arxiv.org/abs/1712.04621.
  13. H. Fonda, “Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn),” J. Ilmu Komput., vol. 9, no. 1, pp. 7–10, 2020, doi: 10.33060/jik/2020/vol9.iss1.144.
  14. N. Ramadhani, J. Hendryli, ) Dyah, and E. Herwindianti, “PENCARIAN OBJEK WISATA BERSEJARAH DI PULAU JAWA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK,” J. Ilmu Komput. dan Sist. Inf., pp. 87–91, 2019.
  15. A. T. HANDOYO, “KLASIFIKASI BAKTERI DENGAN METODE DEEP LEARNING,” UNIVERSITAS HASANUDDIN MAKASSAR, 2021.
  16. C. Nisa’, E. Y. Puspaningrum, and H. Maulana, “Penerapan Metode Convolutional Neural Network untuk Klasifikasi Penyakit Daun Apel pada Imbalanced Data,” Pros. Semin. Nas. Inform. Bela Negara, vol. 1, pp. 169–175, 2020, doi: 10.33005/santika.v1i0.46.
  17. L. Marifatul Azizah, S. Fadillah Umayah, and F. Fajar, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,” Semesta Tek., vol. 21, no. 2, pp. 230–236, 2018, doi: 10.18196/st.212229.