Classification of Rice Plant Diseases Based on Leaf Conditions Using Compact Convolutional Transformers

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Published: 2025-05-26
DOI: https://doi.org/10.34010/komputa.v14i1.15674
Keywords:
Compact Convolutional Transformers (CCT)

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Hestin Nurdiana
Universitas Bina Insan
Novi Lestari
Universitas Bina Insan
Rusdiyanto Rusdiyanto
Universitas Bina Insan
Ahmad Sobri
Universitas Bina Insan

Abstract

Rice (Oryza sativa) is one of the world's main food crops as it serves as a staple food source for the majority of people in various countries, including Indonesia. Optimal rice productivity is highly dependent on plant health, particularly the condition of the leaves, which are susceptible to various diseases. Diseases affecting rice leaves can significantly reduce yields, making early detection and disease management crucial for farmers. Deep learning methods, such as Convolutional Neural Networks (CNNs), have demonstrated excellent performance in image pattern recognition, including plant disease classification based on leaf imagery. One of the latest advancements in this field is Compact Convolutional Transformers (CCT), which combine the strengths of CNNs in capturing local features with the ability of Transformers to understand global relationships between image features. The Compact Convolutional Transformers (CCT) method will be applied to classify rice plant diseases based on leaf images.  This study classifies four categories, namely Normal, Leaf smut, Brown spot, and Bacterial leaf blight. This technology is expected to assist farmers in detecting rice diseases automatically and more rapidly, ultimately enhancing productivity and harvest quality. The study has resulted in a reliable model, achieving an accuracy of 94% with a low loss.

Article Details

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Vol. 14 No. 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika

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How to Cite

Classification of Rice Plant Diseases Based on Leaf Conditions Using Compact Convolutional Transformers. (2025). Komputa : Jurnal Ilmiah Komputer Dan Informatika, 14(1), 139-146. https://doi.org/10.34010/komputa.v14i1.15674

References

[1] U. H. Mubarok, M. Nasiruddin, and D. A. S. H. 3, “Uji Efektivitas Bahan Jebakan Papan Terhadap Keanekaragaman Serangga Pada Tanaman Padi (Oryza sativa L.),” J. Pendidik. Sos. dan Hum., vol. 3, no. 3, pp. 2438–2449, 2024.

[2] S. Suhendar, A. Purnama, and E. Fauzi, “Deteksi Penyakit Pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network,” J. Ilm. Inform. Glob., vol. 14, no. 3, pp. 62–67, 2023.

[3] G. Y. Christiawan, R. A. Putra, A. Sulaiman, E. Poerbaningtyas, S. Widyayuningtias, and K. Kunci, “Penerapan Metode Convolutional Neural Network (CNN) Dalam Mengklasifikasikan Penyakit Daun Tanaman Padi,” J-INTECH(Journal ofInformation Technol., no. 204, pp. 294–306, 2023.

[4] A. C. Milano, A. Yasid, and R. T. Wahyuningrum, “Klasifikasi Penyakit Daun Padi Menggunakan Model Deep Learning Efficientnet-B6,” JITET (Jurnal Inform. dan Tek. Elektro Ter., vol. 12, no. 1, pp. 551–559, 2024.

[5] A. A. Huda, B. Setiaji, and F. R. Hidayat, “Implementasi Gray Level CO-Occurrence Matrix (GLCM) untuk Klasifikasi Penyakit Daun Padi,” J. Pseudocode, vol. 9, no. 1, pp. 33–38, 2022.

[6] K. Penyakit and D. Pisang, “Klasifikasi Penyakit Daun Pisang Menggunakan Convolutional Neural Network (CNN),” J. Teknol. Terpadu Vol., vol. 10, no. 1, pp. 1–6, 2024.

[7] N. Krishnamoorthy, L. V. N. Prasad, C. S. P. Kumar, B. Subedi, H. Baraki, and V. E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning,” Environ. Res., vol. 198, p. 111275, 2021, doi: 10.1016/j.envres.2021.111275.

[8] H. A. Pratiwi, M. Cahyanti, and M. Lamsani, “Implementasi Deep Learning Flower Scanner Menggunakan Metode Convolutional Neural Network,” vol. 25, no. 1, pp. 124–130, 2021, doi: 10.46984/sebatik.v25i1.1297.

[9] A. Raup, W. Ridwan, Y. Khoeriyah, and Q. Y. Zaqiah, “Deep Learning dan Penerapannya dalam Pembelajaran,” JIIP (Jurnal Ilm. Ilmu Pendidikan), vol. 5, no. 9, pp. 3258–3267, 2022.

[10] A. K. Syarif, “Sistem Klasifikasi Penyakit Tanaman Cabai Menggunakan Metode Deep Learning dengan Library Tensorflow Lite,” Univesitas Hasanudin, 2021.

[11] N. Musfirah, “Identifikasi Jenis Serangan Hama pada Buah Kakao menggunakan Image processing,” Univesitas Hasanudin, 2023.

[12] Rizky Alfiansyah, “Deteksi Manipulasi Citra dengan Error Level Anlysis dan Convolutional Neural Network,” Univesitas Hasanudin, 2023.

[13] A. I. Jajja et al., “Compact Convolutional Transformer ( CCT )-Based Approach for Whitefly Attack Detection in Cotton Crops,” Agric. Artic., vol. 12, pp. 1–17, 2022.

[14] A. Hassani et al., “Escaping the Big Data Paradigm with Compact Transformers,” arXiv, 2021.

[15] R. Farkh, G. Oudinet, and Y. Foued, “Image Captioning Using Multimodal Deep Learning Approach,” Comput. Mater. Contin., vol. 81, no. 3, pp. 3951–3968, 2024, doi: 10.32604/cmc.2024.053245.