The metabolism and growth of vegetation are highly dependent on the changes in soil water content. Irrigation scheduling and application of water at the right time and rate are a key aspect for precision irrigation. In this study, the Long Short-Term Memory (LSTM) Neural Network model was studied to predict irrigation prescriptions for 1, 3, 6, 12 and 24 h in advance. Training data for LSTM were collected from a precision irrigation study conducted in Alabama, USA. The prediction estimation of irrigation prescription used soil matric potential data measured within two contrasting soil types. Performance of the LSTM models were evaluated by comparing neural network parameters and prediction capability by soil type. The optimal learning algorithm for each case was also determined. The LSTM Neural Network showed good prediction capabilities for both soil types, with $$R^{2}$$ ranging between 0.82 and 0.98 for one hour ahead prescription and getting smaller as prediction time increases. The irrigation rate prediction was verified by actual observations that demonstrate the suitability of the machine learning technique as a decision-support tool for irrigation scheduling.

中文翻译：

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用于灌溉管理的长短期记忆神经网络：美国阿拉巴马州南部的案例研究

植被的代谢和生长高度依赖于土壤含水量的变化。在正确的时间和速率下安排灌溉和施水是精确灌溉的关键方面。在本研究中，研究了长短期记忆 (LSTM) 神经网络模型来提前预测 1、3、6、12 和 24 小时的灌溉处方。LSTM 的训练数据收集自在美国阿拉巴马州进行的精确灌溉研究。灌溉配方的预测估计使用在两种对比土壤类型中测量的土壤基质势数据。通过比较神经网络参数和土壤类型的预测能力来评估 LSTM 模型的性能。还确定了每种情况的最佳学习算法。LSTM 神经网络对两种土壤类型都显示出良好的预测能力，$R​​^{2}$$ 的范围在 0.82 和 0.98 之间，提前一小时处方，并且随着预测时间的增加而变小。灌溉率预测通过实际观察得到验证，这些观察证明了机器学习技术作为灌溉调度决策支持工具的适用性。