Knowledge Guided Encoder-Decoder Framework Integrating Multiple Physical Models for Agricultural Ecosystem Modeling

2025-05-05

AI Summary

Agricultural ecosystem modeling faces dual challenges: physics-based models suffer from poor generalizability and high parameter uncertainty, while data-driven models lack interpretability, require extensive labeled data, and fail to capture ecological mechanisms among variables. To address these limitations, we propose a knowledge-guided encoder–decoder framework that, for the first time, embeds multiple physics-based model priors into a deep learning architecture. Leveraging a large language model (LLM), our framework uniformly parses heterogeneous inputs and dynamically selects the most appropriate physical submodel. Evaluated on cross-site carbon–nitrogen flux prediction, our method significantly outperforms both standalone physics-based models and black-box deep learning approaches. It achieves superior robustness, high mechanistic interpretability, and low dependency on labeled data. This work establishes a novel paradigm for generalizable, mechanism-aware modeling of agricultural ecosystems.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Agricultural monitoring is critical for ensuring food security, maintaining sustainable farming practices, informing policies on mitigating food shortage, and managing greenhouse gas emissions. Traditional process-based physical models are often designed and implemented for specific situations, and their parameters could also be highly uncertain. In contrast, data-driven models often use black-box structures and does not explicitly model the inter-dependence between different ecological variables. As a result, they require extensive training data and lack generalizability to different tasks with data distribution shifts and inconsistent observed variables. To address the need for more universal models, we propose a knowledge-guided encoder-decoder model, which can predict key crop variables by leveraging knowledge of underlying processes from multiple physical models. The proposed method also integrates a language model to process complex and inconsistent inputs and also utilizes it to implement a model selection mechanism for selectively combining the knowledge from different physical models. Our evaluations on predicting carbon and nitrogen fluxes for multiple sites demonstrate the effectiveness and robustness of the proposed model under various scenarios.

Problem

• Addressing limitations of traditional physical models in agriculture
• Overcoming data-driven models' lack of ecological variable interdependence
• Improving prediction accuracy for crop variables under diverse conditions

Innovation

• Knowledge-guided encoder-decoder for crop prediction
• Integrates multiple physical models' underlying processes
• Uses language model for input processing and model selection