Enhanced Agriculture-informed Neural Network by Domain Knowledge

2026-09-16

AI Summary

研究通过结合农业领域知识（如肥料扩散、土壤呼吸等）改进神经网络模型，以提高农业N2O排放预测的准确性与可解释性。

Abstract

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.

Problem

• Nitrous Oxide Emissions
• Agriculture
• Deep Learning
• Environmental Impact
• Sustainable Farming

Innovation

• Knowledge-enhanced Agriculture-informed Neural Network
• domain knowledge integration
• N2O emissions prediction
• neural-mechanistic framework
• improved generalization