Implementation of Convolutional Neural Network for Rice Leaf Disease Classification to Optimize Farmers’ Decision-Making

Tarq Hilmar Siregar

doi:10.59934/jaiea.v5i2.2137

Authors

Tarq Hilmar Siregar SEKOLAH TINGGI MANAJEMEN INFORMATIKA DAN KOMPUTER (STMIK) TIME

DOI:

https://doi.org/10.59934/jaiea.v5i2.2137

Keywords:

Convolutional Neural Network, Deep Learning, Image Classification, MobileNetV2, Rice Leaf Disease

Abstract

This study aims to develop a rice leaf disease classification model using a Convolutional Neural Network (CNN) with the MobileNetV2 architecture to assist farmers in making accurate decisions. A quantitative approach was employed through experimental methods involving the processing of 3,829 digital images from a publicly available dataset. The results indicate that the developed CNN model effectively classifies six categories of rice leaf conditions with 91% accuracy and was successfully integrated into a web-based application. This research concludes that the implementation of the MobileNetV2 architecture provides a rapid and efficient approach to plant disease diagnosis compared to traditional manual methods.

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References

R. Hilalullaily, N. Kusnadi, and D. Rachmina, “Analisis Efisiensi Usahatani Padi di Jawa dan Luar Jawa, Kajian Prospek Peningkatan Produksi Padi Nasional,” Jurnal Agribisnis Indonesia, vol. 9, no. 2, pp. 143–153, Dec. 2021, doi: 10.29244/jai.2021.9.2.143-153.

R. Deng et al., “Automatic Diagnosis of Rice Diseases Using Deep Learning,” Front. Plant Sci., vol. 12, Aug. 2021, doi: 10.3389/fpls.2021.701038.

N. Istiqomah and M. Murinto, “Klasifikasi Penyakit Tanaman Padi Berbasis Citra Daun Menggunakan Convolutional Neural Network (CNN),” JSTIE (Jurnal Sarjana Teknik Informatika) (E-Journal), vol. 12, no. 1, p. 18, Feb. 2024, doi: 10.12928/jstie.v12i1.27314.

O. Virgantara Putra, M. Zaim Mustaqim, D. Muriatmoko, J. Teknik Informatika, and F. Sains dan Teknologi, “Transfer Learning untuk Klasifikasi Penyakit dan Hama Padi Menggunakan MobileNetV2 Transfer Learning for Rice Disease and Pest Classification using MobileNetV2,” 2023.

Anshul M, “Rice Disease Dataset,” kaggle. Accessed: Aug. 10, 2025. [Online]. Available: https://www.kaggle.com/datasets/anshulm257/rice-disease-dataset

A. C. Milano, “Klasifikasi Penyakit Daun Padi Menggunakan Model Deep Learning EfficientNet-B6,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 1, Jan. 2024, doi: 10.23960/jitet.v12i1.3855.

S. Venu Vasantha, B. Kiranmai, and S. Rama Krishna, “Techniques for Rice Leaf Disease Detection using Machine LearningAlgorithms.” [Online]. Available: www.ijert.org

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Mar. 2019, [Online]. Available: http://arxiv.org/abs/1801.04381

A. Putra, “Klasifikasi Penyakit Daun Cabai Dengan Metode CNN Untuk Deteksi Awal.,” Jurnal Profesi Insinyur Universitas Lampung, vol. 6, no. 1, Jun. 2025, doi: 10.23960/jpi.v6n1.147.

A. M. Lesmana, R. P. Fadhillah, and C. Rozikin, “Identifikasi Penyakit pada Citra Daun Kentang Menggunakan Convolutional Neural Network (CNN),” Jurnal Sains dan Informatika, vol. 8, no. 1, pp. 21–30, Jun. 2022, doi: 10.34128/jsi.v8i1.377.

A. Sanusi Mashuri and A. Sunyoto, “Klasifikasi Penyakit Pada Daun Cabai Menggunakan Arsitektur VGG16,” Journal homepage: Journal of Electrical Engineering and Computer (JEECOM), vol. 6, no. 2, 2024, doi: 10.33650/jeecom.v4i2.

J. Sains dan Teknologi, J. Jumadi, and D. Sartika, “Pengolahan Citra Digital Untuk Identifikasi Objek Menggunakan Metode Hierarchical Agglomerative Clustering”.

M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” Mar. 2016, [Online]. Available: http://arxiv.org/abs/1603.04467

M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Mar. 01, 2023, MDPI. doi: 10.3390/computation11030052.

A. Banks and E. Porcello, Learning React: Functional Web Development with React and Redux. O’Reilly Media, Inc, 2017. Accessed: Aug. 20, 2025. [Online]. Available: https://books.google.co.id/books?id=ycTADgAAQBAJ&printsec=frontcover&hl=id#v=onepage&q&f=false

React.dev, “React The library for Web and Native User Interfaces.” Accessed: Aug. 20, 2025. [Online]. Available: https://react.dev/

D. V. Kornienko, S. V. Mishina, S. V. Shcherbatykh, and M. O. Melnikov, “Principles of securing RESTful API web services developed with python frameworks,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Nov. 2021. doi: 10.1088/1742-6596/2094/3/032016.

Tailwind Labs, “Tailwind CSS - Rapidly Build Modern Websites Without Ever Leaving Your HTML.” Accessed: Aug. 20, 2025. [Online]. Available: https://tailwindcss.com/

“Machine Learning Glossary,” Google Developers. Accessed: Aug. 10, 2025. [Online]. Available: https://developers.google.com/machine-learning/glossary