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Improved accuracy for budbreak and cold hardiness is achieved by a single MTL model that simultaneously learns both tasks, and a variety of MTL architectures are developed and evaluated in both MTL and transfer learning settings.
This work proposes a cold hardiness prediction framework that learns a transferable latent representation by capturing region-specific variation through learned embeddings, and demonstrates that this approach consistently outperforms state-of-the-art cold hardiness prediction methods.
The FAM-LSTM framework offers robust Tb forecasting to support precision heat stress management in vineyards and consistently outperformed all benchmark models across all horizons and input scenarios.
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