Citation

Maniam, Shamala and Tee, Yei Kheng and Memar, Erfan and Wong, Hin Yong and Zaman, Mukter (2026) Smart irrigation management: IoT-based RNN-LSTM model for soil moisture prediction in precision agriculture. Smart Agricultural Technology, 13. p. 101866. ISSN 27723755

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Abstract

This study presents an IoT-enabled smart irrigation management system utilizing subsurface soil moisture sensors and a recurrent neural network–long short-term memory (RNN-LSTM) model to predict soil moisture in realtime for precision agriculture. The proposed system was deployed in Malaysia for six months, achieving a root mean square error (RMSE) of 1.222, a mean absolute error (MAE) of 0.6374, and a coefficient of determination (R²) of 0.6723, explaining approximately 67% of the variance in the observed data. Additionally, 95.49% of predictions fell within ±5% of actual measured values, a tolerance-based metric distinct from classification accuracy. Outlier analysis revealed that the largest residuals occurred during heavy rainfall events, and adopting a robust Huber loss function improved R² to 0.70. The results indicate that the system can effectively support irrigation scheduling, although future work should extend seasonal coverage and address spatial variability in larger fields.

Item Type:	Article
Uncontrolled Keywords:	Precision agriculture, prediction model
Subjects:	S Agriculture > S Agriculture (General)
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2026 04:11
Last Modified:	03 Apr 2026 04:11
URII:	http://shdl.mmu.edu.my/id/eprint/15694

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