Problem
Research questions and friction points this paper is trying to address.
grape berry temperature
heat stress management
multi-step forecasting
Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention
🤖 AI Summary
研究使用集成前馈注意力机制的LSTM模型（FAM-LSTM）来提高葡萄果实温度的多步预测准确性，以支持葡萄园中的精准热应力管理。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
feed-forward attention mechanism
LSTM
multi-step forecasting
grape berry temperature
microclimate data
🔎 Similar Papers
No similar papers found.
