Rice Deep Knowledge Graph-Based Expert System: An Intelligent Solution for Identifying Rice Pests and Diseases

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Published Feb 28, 2026
https://doi.org/10.55043/jaast.v10i1.332
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Vol. 10 No. 1 (2026): Journal of Applied Agricultural Science and Technology

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Muhammad Ariful Furqon
Universitas Jember
https://orcid.org/0000-0002-1031-3567
Muhamad Arief Hidayat
Universitas Jember
Windi Eka Yulia Retnani
Universitas Jember
Gayatri Dwi Santika
Universitas Jember

Abstract

Accurate diagnosis of rice pests and diseases is essential but often challenging using traditional methods, which are time-consuming and prone to human error. In this study, we propose the Rice Deep Knowledge Graph (RiceDKG) Expert System, which integrates deep learning techniques, particularly Long Short Term Memory (LSTM), with a Knowledge Graph to enhance symptom pattern-based diagnosis accuracy. This hybrid approach captures relationships among rice plant symptoms while leveraging systematically constructed domain knowledge. The system was evaluated on a dataset of 25 test cases, encompassing various symptoms such as brown spots, leaf curling, and fungal damage. Evaluation results demonstrate an overall accuracy of 84%, with 21 out of 25 cases correctly diagnosed, compared to expert evaluations. These findings indicate that integrating LSTM with knowledge graphs improves the system's ability to handle diverse diagnostic scenarios.

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Author Biographies

Muhammad Ariful Furqon, Universitas Jember

Department of Informatics, Universitas Jember, Jember, Indonesia

Muhamad Arief Hidayat, Universitas Jember

Department of Informatics, Universitas Jember, Jember, Indonesia

Windi Eka Yulia Retnani, Universitas Jember

Department of Information Technology, Universitas Jember, Jember, Indonesia

Gayatri Dwi Santika, Universitas Jember

Department of Informatics, Universitas Jember, Jember, Indonesia

How to Cite
1.
Furqon MA, Hidayat MA, Retnani WEY, Santika GD. Rice Deep Knowledge Graph-Based Expert System: An Intelligent Solution for Identifying Rice Pests and Diseases. J. appl. agricultural sci. technol. [Internet]. 2026Feb.28 [cited 2026Mar.9];10(1):77-90. Available from: https://www.jaast.org/index.php/jaast/article/view/332

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