Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method


  • Sunu Jatmika Institut Teknologi Dan Bisnis Asia Malang
  • Danang Eka Saputra Institut Teknologi dan Bisnis Asia Malang




Deep Learning, From the Scratch, Inception V3, Rice Leaves Identification, Transfer Learning


Indonesia is an agricultural country where most of the population grows rice and most farmers cannot detect early if there is a pest attack on rice plants . This research discuss about deep learning implementation to classify or identify diseases in rice leaves using mobile application. This system will make users easily to diagnose diseases by displaying diagnostic results in the form of the name of the disease along with its taxonomy, disease description and drug recommendations for disease solutions. There are four classes of leaves used in this research, including healthy leaves, leaf blight, brown spot and potassium deficiency. The design of the model uses two approaches, one of them are modeling convolutional neural network from the scratch and modeling with transfer learning using inception v3 architecture. Both models will go through training process to produce a model that is ready to be used for classification. In application testing, a comparison is made between two models. From the tests that have been carried out, it is concluded that the system with model made using transfer learning approach, produce good accuracy with an accuracy of 90%. Meanwhile the System with the other model gain an accuracy of 62%. So when the data used in research are extremely low, it is best to use transfer learning as an approach to design a mode.

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Abadi, M., Chu, A., & Goodfellow, I. (2016). Deep Learning with Differential Privacy. Conference on Computer and Communications Securit (pp. 1-14). New York: Association for Computing Machinery.

Alwanda, M. R., Ramadhan, R. P., & Alamsyah, D. (2020). Implementasi Metode Convolutional Neural Network. Jurnal Algoritme, 45 - 56.

Fausset, L. (1993). Fundamentals of Neural Networks: Architectures, Algorithms And Application. Glossary.

Hidayat, A., Darusalam, U., & Darusalam. (2019). Detection of Disease on Corn Plants Using Convolutional Neural. Jurnal Ilmu Komputer dan Informasi, 51-57.

Hidayatulah, P. (2017). Pengolahan Citra Digital Teori dan Aplikasinya. Bandung: Informatika Bandung.

Kadir, A., & Adhi, S. (2013). Teori dan Aplikasi Pengolahan Citra. Yogyakarta: Andi Offset.

Minarni, & Warman, I. (2017). Sistem Pakar Identifikasi Penyakit Tanaman Padi. (pp. D28 - D32). Yogyakarta: Seminar Nasional Aplikasi Teknologi Informasi (SNATi).

Nisa, C., Puspaningrum, E. Y., & Yulia, H. (2020). Penerapan Metode Convolutional Neural Network untuk. (pp. 169 - 175). Seminar Nasional Informatika Bela Negara (SANTIKA).

Pandjaitan, & Lanny W. (2007). Dasar-Dasar Komputasi Cerdas. Yogyakarta: Andi Offset.

Primartha, R. (2018). Belajar Machine Learning Teori dan Praktik. Bandung: Informatika Bandung.

Russell, S. &. (2010). Artificial Intelligency A Modern Approach (Third ed.). Pearson Education,Inc.

Santosa, A., & Ariyanto, G. (2018). Implementasi Deep Learning Berbasis Keras untuk Pengenalan Wajah. Jurnal Teknik Elektro, 15-21.

Saputra, R. A., Wasyianti, S., Adi, S., & Saefudin, D. F. (2021). Penerapan Algoritma Convolutional Neural Network. Jurnal Swabumi, 185-189.

Shah, h. (2018). Deep learning: An introduction to framework. International Journal Of Advanced Research, Ideas and Innovations In Technology, 5-8.

Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. University College London, 1 - 10


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How to Cite

Jatmika, S. ., & Saputra, D. E. . (2022). Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 2008-2016. https://doi.org/10.33395/sinkron.v7i3.11540