基于领域知识增强的农业信息神经网络
Enhanced Agriculture-informed Neural Network by Domain Knowledge
- University of Ottawa（渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对农业N2O排放预测难题，提出知识增强的农业信息神经网络（KAINN），融合肥料扩散等物理知识，在多个架构上降低误差并提升可解释性与泛化能力。
中文摘要 AI 辅助
准确预测农业中一氧化二氮（N2O）排放对于评估环境影响和支持可持续农业至关重要。然而，由于N2O排放源于土壤性质、气候、生化过程和管理实践之间的复杂相互作用，且高质量观测数据有限，预测仍然困难。深度学习模型能够捕捉非线性关系，但往往缺乏物理可解释性，并可能在跨环境条件下泛化能力较差。我们提出了知识增强的农业信息神经网络（KAINN），这是一种混合神经-机理框架，通过纳入关于肥料扩散、土壤呼吸和水填充孔隙度的领域知识，扩展了农业信息神经网络。我们使用CNN、LSTM和Transformer架构，在多个生长季节和输入特征配置下评估了KAINN。结果表明，与纯数据驱动模型和原始AINN相比，KAINN通常提供更低的均方根误差和平均绝对误差，以及更高的R平方值。对学习界面的分析还显示，参数轨迹更平滑、物理上更一致，且不确定性降低。这些发现表明，将环境知识纳入神经网络可以提高农业N2O排放预测的可靠性、可解释性和泛化能力。
英文摘要
Accurate prediction of nitrous oxide (N2O) emissions from agriculture is important for assessing environmental impacts and supporting sustainable farming. However, prediction remains difficult because N2O emissions result from complex interactions among soil properties, climate, biochemical processes, and management practices, while high-quality observations are limited. Deep learning models can capture nonlinear relationships but often lack physical interpretability and may generalize poorly across environmental conditions. We propose the Knowledge-enhanced Agriculture-informed Neural Network (KAINN), a hybrid neural-mechanistic framework that extends the Agriculture-informed Neural Network by incorporating domain knowledge about fertilizer diffusion, soil respiration, and water-filled porosity. We evaluate KAINN using CNN, LSTM, and Transformer architectures across multiple growing seasons and input-feature configurations. The results show that KAINN generally provides lower root mean square error and mean absolute error and higher R-squared values than purely data-driven models and the original AINN. Analysis of the learned interfaces also shows smoother and more physically consistent parameter trajectories with reduced uncertainty. These findings demonstrate that incorporating environmental knowledge into neural networks can improve the reliability, interpretability, and generalization of agricultural N2O-emission predictions.
