Integrating Recurrent Neural Networks and Deep Q-Networks for Precision Irrigation Control

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dc.contributor.authorJogo, Fransiksus Serfian
dc.contributor.authorWahyunggoro, Oyas
dc.contributor.authorMustika, I. Wayan
dc.contributor.authorWoraratpanya, Kuntpong
dc.date.accessioned2026-08-06T10:48:31Z
dc.date.available2026-08-06T10:48:31Z
dc.date.issued2025-01-01
dc.description.abstractAgricultural irrigation accounts for a substantial portion of global water consumption, necessitating intelligent irrigation strategies. Conventional systems, such as fixed irrigation or evapotranspiration (ET)-based control, often exhibit inefficiencies due to reliance on static measurements and empirical farmer knowledge, lacking adaptive decision-making capabilities. To address this challenge, this paper presents an IoT-based framework integrating Recurrent Neural Networks (RNNs) for weather prediction and Deep Q-Networks (DQNs) for adaptive irrigation decisions. The RNN achieves high-precision forecasts for solar radiation, rainfall, and reference evapotranspiration (ETo) with determination coefficients (R<sup>2</sup>) of 0.819,0.915, and 0.892, respectively, surpassing the other two neural network models in our tests. Simultaneously, the DQN agent learns irrigation policies that reduce water consumption by 16.6-25.3% compared to conventional methods while maintaining post-irrigation soil moisture above the critical threshold (V<inf>mad</inf>=0.50) in testbed experiments. The proposed RNN-DQN architecture demonstrates significant improvements in water-use efficiency and plant health maintenance, offering a robust solution for smart agriculture in water-limited, data-scarce arid regions.
dc.identifier.citationProceedings of the International Conference on Information Technology and Electrical Engineering Icitee, 2025
dc.identifier.doi10.1109/ICITEE66631.2025.11338380
dc.identifier.issn27660419
dc.identifier.other2-s2.0-105034167040
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16271
dc.sourceProceedings of the International Conference on Information Technology and Electrical Engineering Icitee
dc.subjectDeep QNetworks (DQNs)
dc.subjectPrecision Irrigation
dc.subjectRecurrent Neural Network (RNNs)
dc.subjectWater-Use Efficiency
dc.titleIntegrating Recurrent Neural Networks and Deep Q-Networks for Precision Irrigation Control
dc.typeConference Paper

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