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<p>Water-saving irrigation is critical for improving water use efficiency and ensuring sustainable food production. However, accurate prediction of farmland water demand remains difficult because soil, crop, and meteorological processes interact across space, depth, and time. To address this problem, we develop an AI-STGNN-based irrigation decision framework in which AI-STGNN serves as the core predictive model. The main methodological contribution is a dynamic three-dimensional (3D) graph that represents vertical soil heterogeneity and time-varying hydrological connectivity, while channel-wise meteorological attention and multi-scale temporal memory are incorporated as agro-hydrology-specific enhancements. On the held-out 2021–2022 test set, AI-STGNN achieved RMSE, MAE, and R² values of 3.63 vol%, 2.88 vol%, and 0.89, respectively, reducing RMSE by 7.2% relative to STGCN and by 16.7% relative to LSTM. Under heavy rainfall, its RMSE increased by 45%, compared with 59–70% for the baseline models. In the two-season field comparison across three representative farms, the AI-STGNN-guided strategy reduced irrigation water use by 12.3–15.7%, increased crop yield by 3.5–4.2%, and improved water use efficiency by 13.7–17.8% relative to conventional empirical irrigation. Because the field comparison covers only two growing seasons and three sites, these practical gains should be interpreted as preliminary rather than definitive. Overall, the proposed framework provides a physically informed, data-driven basis for dynamic water-demand prediction and irrigation scheduling in the studied Midwest cropping system.</p>

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Optimization of water-saving irrigation decision using dynamic spatiotemporal graph neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Yilong Fang,
  • Junlin Zheng,
  • Xiaojun Shen,
  • Chao Zhang,
  • Kadambot H.M. Siddique

摘要

Water-saving irrigation is critical for improving water use efficiency and ensuring sustainable food production. However, accurate prediction of farmland water demand remains difficult because soil, crop, and meteorological processes interact across space, depth, and time. To address this problem, we develop an AI-STGNN-based irrigation decision framework in which AI-STGNN serves as the core predictive model. The main methodological contribution is a dynamic three-dimensional (3D) graph that represents vertical soil heterogeneity and time-varying hydrological connectivity, while channel-wise meteorological attention and multi-scale temporal memory are incorporated as agro-hydrology-specific enhancements. On the held-out 2021–2022 test set, AI-STGNN achieved RMSE, MAE, and R² values of 3.63 vol%, 2.88 vol%, and 0.89, respectively, reducing RMSE by 7.2% relative to STGCN and by 16.7% relative to LSTM. Under heavy rainfall, its RMSE increased by 45%, compared with 59–70% for the baseline models. In the two-season field comparison across three representative farms, the AI-STGNN-guided strategy reduced irrigation water use by 12.3–15.7%, increased crop yield by 3.5–4.2%, and improved water use efficiency by 13.7–17.8% relative to conventional empirical irrigation. Because the field comparison covers only two growing seasons and three sites, these practical gains should be interpreted as preliminary rather than definitive. Overall, the proposed framework provides a physically informed, data-driven basis for dynamic water-demand prediction and irrigation scheduling in the studied Midwest cropping system.