用物理约束神经网络预测作物减产，兼具精度与可解释性
Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
- 结合卫星影像与物理模型，像素级估算作物需水与缺水敏感度
- 在真实数据上达到R²=0.77，性能优于或媲美先进深度学习模型
- 输出结果符合农业物理规律，适合政策制定与农事决策
为应对气候变化，评估极端天气下的作物产量表现对保障粮食安全至关重要。作物模拟模型虽符合物理过程、具备可解释性，但预测能力常不足；而机器学习模型虽强大且可扩展，却作为黑箱运行，不遵循作物生长物理规律。为此，本文提出一种新方法，在像素级估计作物需水量及对缺水的敏感性，通过求解作物产量对缺水响应的物理方程，并引入改进损失函数，实现基于物理原理的减产预测。利用哨兵-2卫星影像、气候数据、模拟用水数据及像素级产量数据，模型在测试中取得最高R²=0.77，表现匹配或超越当前最优模型（如RNNs与Transformers）。同时输出具有可解释性与物理一致性，有助于产业界、政策制定者和农户应对极端气候。
原文摘要 · Abstract (English)
In response to climate change, assessing crop productivity under extreme weather conditions is essential to enhance food security. Crop simulation models, which align with physical processes, offer explainability but often perform poorly. Conversely, machine learning (ML) models for crop modeling are powerful and scalable yet operate as black boxes and lack adherence to crop growths physical principles. To bridge this gap, we propose a novel method that combines the strengths of both approaches by estimating the water use and the crop sensitivity to water scarcity at the pixel level. This approach enables yield loss estimation grounded in physical principles by sequentially solving the equation for crop yield response to water scarcity, using an enhanced loss function. Leveraging Sentinel-2 satellite imagery, climate data, simulated water use data, and pixel-level yield data, our model demonstrates high accuracy, achieving an R2 of up to 0.77, matching or surpassing state-of-the-art models like RNNs and Transformers. Additionally, it provides interpretable and physical consistent outputs, supporting industry, policymakers, and farmers in adapting to extreme weather conditions.
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