AgriPINN: A Process-Informed Neural Network for Interpretable and Scalable Crop Biomass Prediction Under Water Stress

2026-01-22

AI Summary

This study addresses the limited interpretability of data-driven models and the scalability challenges of process-based models in predicting aboveground crop biomass under water stress. To bridge this gap, we propose a deep learning approach that integrates biophysical mechanisms by embedding crop growth differential equations as hard constraints within a physics-informed neural network (PINN) framework. Without direct supervision, the model jointly leverages remote sensing and meteorological data to simultaneously recover latent variables such as leaf area index and absorbed photosynthetically active radiation. Pretrained across 397 regions in Germany and fine-tuned with field experiments, the method achieves up to a 43% reduction in RMSE compared to state-of-the-art deep learning models and the LINTUL5 process model, substantially improving prediction accuracy, computational efficiency, and physiological interpretability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Low Level & Physics-based Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate prediction of crop above-ground biomass (AGB) under water stress is critical for monitoring crop productivity, guiding irrigation, and supporting climate-resilient agriculture. Data-driven models scale well but often lack interpretability and degrade under distribution shift, whereas process-based crop models (e.g. DSSAT, APSIM, LINTUL5) require extensive calibration and are difficult to deploy over large spatial domains. To address these limitations, we propose AgriPINN, a process-informed neural network that integrates a biophysical crop-growth differential equation as a differentiable constraint within a deep learning backbone. This design encourages physiologically consistent biomass dynamics under water-stress conditions while preserving model scalability for spatially distributed AGB prediction. AgriPINN recovers latent physiological variables, including leaf area index (LAI), absorbed photosynthetically active radiation (PAR), radiation use efficiency (RUE), and water-stress factors, without requiring direct supervision. We pretrain AgriPINN on 60 years of historical data across 397 regions in Germany and fine-tune it on three years of field experiments under controlled water treatments. Results show that AgriPINN consistently outperforms state-of-the-art deep-learning baselines (ConvLSTM-ViT, SLTF, CNN-Transformer) and the process-based LINTUL5 model in terms of accuracy (RMSE reductions up to $43\%$) and computational efficiency. By combining the scalability of deep learning with the biophysical rigor of process-based modeling, AgriPINN provides a robust and interpretable framework for spatio-temporal AGB prediction, offering practical value for planning of irrigation infrastructure, yield forecasting, and climate-adaptation planning.

Problem

• crop biomass prediction
• water stress
• process-based modeling
• data-driven models
• spatio-temporal prediction

Innovation

• process-informed neural network
• biophysical constraint
• crop biomass prediction
• water stress
• interpretable deep learning