将作物生长物理方程融入神经网络，提升干旱下作物生物量预测的准确性和可解释性。
AgriPINN: A Process-Informed Neural Network for Interpretable and Scalable Crop Biomass Prediction Under Water Stress
- 用微分方程作为可微约束，让神经网络学习符合生理规律的生长动态。
- 在德国397个区域数据上预训练，三年田间实验中误差比基准降低最高43%。
- 适合农业气候适应、灌溉规划和产量预测等需要可解释模型的场景。
在干旱条件下精准预测作物地上生物量（AGB）对监测产量、指导灌溉和支撑气候韧性农业至关重要。数据驱动模型虽可扩展但缺乏可解释性且易受分布偏移影响，而过程模型（如DSSAT、APSIM、LINTUL5）需大量校准，难以大范围部署。为此，我们提出AgriPINN，一种融合生物物理作物生长微分方程的可微约束的神经网络，使模型在干旱条件下保持生理一致性的同时，仍具备空间分布式预测的可扩展性。AgriPINN无需直接监督即可恢复叶面积指数（LAI）、吸收光合有效辐射（PAR）、辐射利用效率（RUE）及水分胁迫因子等潜在生理变量。我们在德国397个区域的60年历史数据上预训练，并在三年受控水处理的田间实验数据上微调。结果表明，AgriPINN在准确率（RMSE降低最高达43%）和计算效率上均优于现有深度学习模型（ConvLSTM-ViT、SLTF、CNN-Transformer）和过程模型LINTUL5。该方法结合了深度学习的可扩展性与过程模型的生物物理严谨性，为时空生物量预测提供了鲁棒且可解释的框架，对灌溉基础设施规划、产量预测和气候适应策略具有实际价值。
原文摘要 · Abstract (English)
Accurate prediction of crop above-ground biomass (AGB) under water stress is critical for monitoring crop productivity, guiding irrigation, and supporting climate-resilient agriculture. Data-driven models scale well but often lack interpretability and degrade under distribution shift, whereas process-based crop models (e.g. DSSAT, APSIM, LINTUL5) require extensive calibration and are difficult to deploy over large spatial domains. To address these limitations, we propose AgriPINN, a process-informed neural network that integrates a biophysical crop-growth differential equation as a differentiable constraint within a deep learning backbone. This design encourages physiologically consistent biomass dynamics under water-stress conditions while preserving model scalability for spatially distributed AGB prediction. AgriPINN recovers latent physiological variables, including leaf area index (LAI), absorbed photosynthetically active radiation (PAR), radiation use efficiency (RUE), and water-stress factors, without requiring direct supervision. We pretrain AgriPINN on 60 years of historical data across 397 regions in Germany and fine-tune it on three years of field experiments under controlled water treatments. Results show that AgriPINN consistently outperforms state-of-the-art deep-learning baselines (ConvLSTM-ViT, SLTF, CNN-Transformer) and the process-based LINTUL5 model in terms of accuracy (RMSE reductions up to $43\%$) and computational efficiency. By combining the scalability of deep learning with the biophysical rigor of process-based modeling, AgriPINN provides a robust and interpretable framework for spatio-temporal AGB prediction, offering practical value for planning of irrigation infrastructure, yield forecasting, and climate-adaptation planning.
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