{"slug": "multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed", "title": "Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention", "summary": "Researchers developed a feed-forward attention mechanism integrated with a Long Short-Term Memory network (FAM-LSTM) for multi-step grape berry temperature forecasting, achieving mean absolute errors as low as 0.51°C using in-vineyard data. The model outperformed LSTM, GRU, RNN, and Random Forest across horizons from 15 minutes to 72 hours in tests at Prosser, WA, with data from 2023–2025. The study, posted on arXiv, supports precision heat stress management in vineyards.", "body_md": "arXiv:2608.29008v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed", "canonical_source": "https://www.machinebrief.com/news/multi-step-forecasting-of-grape-berry-temperature-based-on-l-ani7", "published_at": "2026-09-01 04:00:00+00:00", "updated_at": "2026-09-01 05:53:19.187549+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv", "FAM-LSTM", "LSTM", "GRU", "RNN", "Random Forest", "Prosser, WA"], "alternates": {"html": "https://wpnews.pro/news/multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed", "markdown": "https://wpnews.pro/news/multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed.md", "text": "https://wpnews.pro/news/multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed.txt", "jsonld": "https://wpnews.pro/news/multi-step-forecasting-of-grape-berry-temperature-based-on-lstm-model-with-feed.jsonld"}}