Title: Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting Show Chinese title

Title: 探索物理信息神经网络在作物产量损失预测中的应用

Abstract: 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. Show Chinese abstract

Abstract: 响应气候变化，评估极端天气条件下的作物生产力对于提高粮食安全至关重要。 与物理过程一致的作物模拟模型提供了可解释性，但通常表现不佳。 相反，用于作物建模的机器学习（ML）模型功能强大且可扩展，但作为黑箱运行，并不遵循作物生长的物理原理。 为了弥补这一差距，我们提出了一种新方法，通过在像素级别估计水分利用和作物对水分短缺的敏感性，结合两种方法的优势。 该方法通过依次求解作物产量对水分短缺的方程，使用增强的损失函数，实现了基于物理原理的产量损失估算。 利用Sentinel-2卫星图像、气候数据、模拟的水分利用数据和像素级产量数据，我们的模型表现出高准确性，R2值高达0.77，与最先进的模型如RNNs和Transformers相当或超越。 此外，它提供了可解释且物理一致的输出，支持产业界、政策制定者和农民适应极端天气条件。