Application of the Convolutional Neural Network (CNN) Method in Classifying Rice Plant Leaf Diseases
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Abstract
Rice is a staple crop in Indonesia. Most farmers choose rice as the main crop for agricultural land. Starting from the land to the tropical climate that occurs in Indonesia, it is very suitable for rice plants. Among these supports arise obstacles faced by farmers. Rice leaf diseases include Brownspot, Blas, Bacterial Leaf Blight (HDB). Classification of these diseases can be done using the CNN (Convolutional Neural Network) method. So far, the detection process for rice plant leaf diseases has been done manually. The CNN method can detect images from pixel to pixel so it is considered effective for detecting disease from images alone. This research uses a dataset of 1630 data which is divided into 3 disease classes. This research compares the number of epochs and uses the CNN InceptionV3 architecture. The results of this research show very good results with a lift of 98% with data that is not overfitting.
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Articles
References
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Alidrus, S. A., Musthafa, A., & Putra, O. V. (2021). Deteksi Penyakit Pada Daun Tanaman Padi Menggunakan Metode Convolutional Neural Network. SENAMIKA.
Bari, B. S., Islam, M. N., Rashid, M., Hasan, M. J., Razman, M. A. M., Musa, R. M., Nasir, A. F. A., & Majeed, A. P. P. A. (2021). A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Computer Science, 7. https://doi.org/10.7717/PEERJ-CS.432
Bhatt, P., Sarangi, S., Shivhare, A., Singh, D., & Pappula, S. (2019). Identification of diseases in corn leaves using convolutional neural networks and boosting. ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. https://doi.org/10.5220/0007687608940899
Hidayat, A., Darusalam, U., & Irmawati, I. (2019). Detection Of Disease On Corn Plants Using Convolutional Neural Network Methods. Jurnal Ilmu Komputer Dan Informasi, 12(1). https://doi.org/10.21609/jiki.v12i1.695
Ilahiyah, S., & Nilogiri, A. (2018). Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network. JUSTINDO (Jurnal Sistem Dan Teknologi Informasi Indonesia), 3(2).
Islam, A., Islam, R., Haque, S. M. R., Islam, S. M. M., & Khan, M. A. I. (2021). Rice leaf disease recognition using local threshold based segmentation and deep CNN. International Journal of Intelligent Systems & Applications, 13(5).
Jinan, A., & Hayadi, B. H. (2022). Klasifikasi Penyakit Tanaman Padi Mengunakan Metode Convolutional Neural Network Melalui Citra Daun (Multilayer Perceptron). Journal of Computer and Engineering Science, 37–44.
Khoiruddin, M., Junaidi, A., & Saputra, W. A. (2022). Klasifikasi Penyakit Daun Padi Menggunakan Convolutional Neural Network. Journal of Dinda : Data Science, Information Technology, and Data Analytics, 2(1). https://doi.org/10.20895/dinda.v2i1.341
Priyangka, A. A. J. V., & Kumara, I. M. S. (2021). Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2). https://doi.org/10.24843/lkjiti.2021.v12.i02.p06
Rasjava, A. R., Sugiyarto, A. W., Kurniasari, Y., & Ramadhan, S. Y. (2020). Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN). Proceeding International Conference on Science and Engineering, 3. https://doi.org/10.14421/icse.v3.535
Saputra, R. A., Wasiyanti, S., Supriyatna, A., & Saefudin, D. F. (2021). Penerapan Algoritma Convolutional Neural Network Dan Arsitektur MobileNet Pada Aplikasi Deteksi Penyakit Daun Padi. Swabumi, 9(2). https://doi.org/10.31294/swabumi.v9i2.11678
Situmorang, W., & Jannah, M. (2020). Implementasi Jaringan Syaraf Tiruan Memprediksi Hasil Panen Padi Pada Desa Pagar Jati Dengan Metode Backpropagation. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 3(1.1), 167–175.
Sudadi, S., Sumarno, S., & Handi, W. (2015). Pengaruh Pupuk Organik Berbasis Azolla, Fosfat Alam dan Abu Sekam terhadap Hasil Padi dan Sifat Kimia Tanah Alfisol. Sains Tanah-Journal of Soil Science and Agroclimatology, 11(2), 77–84.
Yuliany, S., & Rachman, A. N. (2022). Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Buana Informatika, 13(1), 54–65.