用注意力机制提升葡萄果温预测精度，助力精准防热应激
Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention
- 结合注意力机制的LSTM模型，捕捉温度变化关键时序特征
- 在288个时间步（72小时）内，误差低于1.70℃，优于其他模型
- 用园内微气候数据可进一步降低误差，适合葡萄种植管理
准确预测葡萄果温（Tb）对及时开展热应激管理至关重要。本研究提出一种融合前馈注意力机制的长短期记忆网络（FAM-LSTM），用于多步高分辨率果温预测。模型基于美国华盛顿州普罗斯尔2023与2024年环境数据训练，并在2025年夏季数据上验证。对比了LSTM、GRU、RNN和随机森林（RF）等模型，在15分钟至72小时（288个时间步）的多个预测时长远中进行评估。分别采用邻近气象站观测数据和园内微气候实测数据作为输入。FAM-LSTM在所有时延和输入场景下均表现最优。引入园内微气候数据显著提升了长时预测精度。使用外部气象站数据时，平均绝对误差（MAE）为0.58–1.70℃，均方根误差（RMSE）为0.65–2.07℃；使用园内数据时，MAE为0.51–1.55℃，RMSE为0.71–1.87℃。误差分析显示，日间高峰时段（11:00–18:00）预测不确定性最高，且随预测时延增加而上升。整体表明，FAM-LSTM框架可为葡萄园精准热应激管理提供可靠支持。
原文摘要 · Abstract (English)
Accurate forecasting of grape berry temperature (Tb) is essential for enabling timely heat stress management in vineyards. In this study, a feed-forward attention mechanism integrated with a Long Short-Term Memory network (FAM-LSTM) was developed and evaluated for multi-step, high-resolution Tb prediction. Models were trained using environmental data from 2023 and 2024 at Prosser, WA, USA, and validated on 2025 summer data. FAM-LSTM was benchmarked against LSTM, GRU, RNN, and Random Forest (RF) across horizons ranging from 15 minutes to 72 hours (288 time steps). Two input scenarios were evaluated: nearest open-field weather station observations and in-vineyard microclimate measurements. FAM-LSTM consistently outperformed all benchmark models across all horizons and input scenarios. Incorporating in-vineyard microclimate data significantly improved forecasting accuracy at longer horizons. Using open-field data, FAM-LSTM achieved MAE and RMSE ranges of 0.58 to 1.70 deg C and 0.65 to 2.07 deg C, respectively. In-vineyard observations further improved performance, with MAE and RMSE in the ranges of 0.51 to 1.55 deg C and 0.71 to 1.87 deg C. Error analysis showed prediction uncertainty was highest during peak daytime periods (11:00 to 18:00) and increased progressively with forecast horizon. Overall, the FAM-LSTM framework offers robust Tb forecasting to support precision heat stress management in vineyards.
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