用神经网络动态调整生物模型参数，提升作物预测精度与真实性
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
- 用神经网络预测生物模型的参数，实现可解释的精准预测
- 在真实与合成数据上，物候期预测准确率提升60%，抗寒性提升40%
- 适合数据少的农业场景，尤其适用于多品种作物管理
准确预测作物状态（如物候阶段和抗寒性）对及时开展灌溉、施肥和冠层管理至关重要，有助于优化产量与品质。传统生物物理模型虽能实现全季预测，但难以满足精准农作需求；深度学习方法虽有潜力，却常产生不合理的生物学结果，且依赖大量数据。本文提出一种混合建模框架，利用神经网络对可微分的生物物理模型进行参数化，并通过多任务学习在数据有限条件下实现不同作物品种间的高效数据共享。通过预测生物模型的参数，该方法在保持生物合理性的同时提升了预测精度。在真实世界与合成数据上的实证评估表明，相比现有生物物理模型，本方法在物候期预测上准确率提高60%，抗寒性预测提高40%。
原文摘要 · Abstract (English)
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a \emph{hybrid modeling} approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the \emph{parameters} of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60\% for phenology and 40\% for cold hardiness compared to deployed biophysical models.
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