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.

翻译：水分胁迫下作物地上生物量（AGB）的准确预测对于监测作物生产力、指导灌溉实践及支持气候适应性农业至关重要。数据驱动模型具有良好的可扩展性，但往往缺乏可解释性，且在分布偏移下性能下降；而基于过程的作物模型（如DSSAT、APSIM、LINTUL5）需要大量校准，难以在大空间范围部署。为应对这些局限，本文提出AgriPINN——一种过程信息神经网络，它将作物生长的生物物理微分方程作为可微分约束整合到深度学习主干网络中。该设计促使模型在水分胁迫条件下生成符合生理规律的生物量动态，同时保持模型在空间分布式AGB预测中的可扩展性。AgriPINN能够恢复包括叶面积指数（LAI）、吸收的光合有效辐射（PAR）、辐射利用效率（RUE）及水分胁迫因子在内的潜在生理变量，且无需直接监督。我们使用德国397个区域60年的历史数据对AgriPINN进行预训练，并在受控水分处理下的三年田间试验数据上进行微调。结果表明，在预测精度（RMSE降低幅度高达$43\%$）与计算效率方面，AgriPINN均持续优于当前最先进的深度学习基线模型（ConvLSTM-ViT、SLTF、CNN-Transformer）以及基于过程的LINTUL5模型。通过融合深度学习的可扩展性与基于过程建模的生物物理严谨性，AgriPINN为时空AGB预测提供了一个鲁棒且可解释的框架，为灌溉基础设施规划、产量预报及气候适应规划提供了实用价值。