English(EN) Multi-Step Forecasting of Grape Berry Temperature based on LSTM Model with Feed-Forward Attention

新型FAM-LSTM模型改进葡萄浆果温度预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的FAM-LSTM模型，将前馈注意力机制与长短期记忆网络（LSTM）相结合，以准确预测葡萄浆果的温度。该模型在各种预测范围和数据输入场景下，始终优于LSTM、GRU、RNN和随机森林等现有方法。研究发现，纳入园内微气候测量数据可显著提高预测精度，尤其是在较长的预测期间，为葡萄园的精准热应力管理提供了强大工具。 AI

影响通过改进作物管理的温度预测，增强精准农业。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Srikanth Gorthi, L. G. Divyanth, Dattatray Bhalekar, Markus Keller, Lav Khot · 2026-09-01 04:00

基于带前馈注意力的LSTM模型的葡萄浆果温度多步预测

arXiv:2608.29008v1 Announce Type: new 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)…

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基于带前馈注意力的LSTM模型的葡萄浆果温度多步预测

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