TY  - JOUR
AU  - Zhang, Hongliang
AU  - Xu, Hongxi
AU  - Zhu, Meimei
AU  - Zeng, Min
AU  - Zhao, Xiaojing
AU  - Wu, Hongfeng
PY  - 2025
DA  - 2025/08/27
TI  - Research on Precision Paddy Field Irrigation Control Technology Based on Multi-Source Sensing Hybrid Model
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 1
IS  - 1
SP  - 14
EP  - 23
DO  - 10.62762/DIA.2025.680966
UR  - https://www.icck.org/article/abs/DIA.2025.680966
KW  - precision irrigation
KW  - hybrid model
KW  - multi-source data fusion
KW  - collaborative control
AB  - Rice cultivation accounts for 65% of agricultural water consumption in China but suffers from low irrigation water use efficiency (WUE ≈ 0.80kg/m^3) and severe nitrogen and phosphorus loss, contributing up to 30% of agricultural non-point source pollution. To address these core issues, this study developed a multi-source collaborative sensing-based Intelligent Precision Paddy Irrigation Control System (IPICS). The system integrates satellite and UAV remote sensing with IoT technologies to construct a ``sky-space-ground'' three-dimensional monitoring system, couples the FAO-56 Penman-Monteith model with LSTM deep learning algorithms to establish a dynamic irrigation decision model, and integrates a self-developed solar-powered intelligent sluice gate (AG-SOLAR-M7) for precise canal water control. A two-year field validation (2023-2024) was conducted at Youyi Farm (131.8121°E, 46.7825°N), comparing the Intelligent Precision Irrigation Control System (IPICS) with conventional flooding(each 40 ha, 3 replicates). Results demonstrated that: (1) IPICS reduced irrigation by 25.3% (625±32 mm vs. 837±41 mm, p
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Zhang2025Research,
  author = {Hongliang Zhang and Hongxi Xu and Meimei Zhu and Min Zeng and Xiaojing Zhao and Hongfeng Wu},
  title = {Research on Precision Paddy Field Irrigation Control Technology Based on Multi-Source Sensing Hybrid Model},
  journal = {Digital Intelligence in Agriculture},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {14-23},
  doi = {10.62762/DIA.2025.680966},
  url = {https://www.icck.org/article/abs/DIA.2025.680966},
  abstract = {Rice cultivation accounts for 65\% of agricultural water consumption in China but suffers from low irrigation water use efficiency (WUE ≈ 0.80kg/m^3) and severe nitrogen and phosphorus loss, contributing up to 30\% of agricultural non-point source pollution. To address these core issues, this study developed a multi-source collaborative sensing-based Intelligent Precision Paddy Irrigation Control System (IPICS). The system integrates satellite and UAV remote sensing with IoT technologies to construct a ``sky-space-ground'' three-dimensional monitoring system, couples the FAO-56 Penman-Monteith model with LSTM deep learning algorithms to establish a dynamic irrigation decision model, and integrates a self-developed solar-powered intelligent sluice gate (AG-SOLAR-M7) for precise canal water control. A two-year field validation (2023-2024) was conducted at Youyi Farm (131.8121°E, 46.7825°N), comparing the Intelligent Precision Irrigation Control System (IPICS) with conventional flooding(each 40 ha, 3 replicates). Results demonstrated that: (1) IPICS reduced irrigation by 25.3\% (625±32 mm vs. 837±41 mm, p},
  keywords = {precision irrigation, hybrid model, multi-source data fusion, collaborative control},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}