Physics-Informed Neural Network Methods for Predicting Plant Height Development

2026/01/14 by Yingjie Shao, Fred A. van Eeuwijk, Carel F.W. Peeters +3 · 1 voice
Agricultural and Biological Sciences · Environmental Science · #Greenhouse Technology and Climate Control #Plant Water Relations and Carbon Dynamics #Smart Agriculture and AI

paper · doi:10.64898/2026.01.14.699475

openalex publication_date 2026/01/14 · openalex created_date 2026/01/16 · openalex updated_date 2026/07/15

Abstract

ABSTRACT Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. Highlights Integrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited.

Citations

• A Simplex Method for Function Minimization
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• 125 BPASを用いた椎骨脳底動脈解離に対する撮像法の検討(MR検査 頭頚部MRA,一般研究発表,日本放射線技術学会 第34回秋季学術大会)
• SciPy 1.0: fundamental algorithms for scientific computing in Python
• Physics-informed machine learning
• Phenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements
• Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
• Physics-informed neural networks with adaptive loss weighting algorithm for solving partial differential equations
• Physics-informed Neural Networks with Unknown Measurement Noise
• Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
• Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization
• Three Ways to Solve Partial Differential Equations with Neural Networks\n -- A Review
• Dive into Deep Learning
• The Early Phase of Neural Network Training
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Tunability: Importance of Hyperparameters of Machine Learning Algorithms
• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• Imposing Hard Constraints on Deep Networks: Promises and Limitations
• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
• Long Short-Term Memory

Discussions

• Physics-Informed Neural Network Methods for Predicting Plant Height Development by Yingjie Shao, Fred van Eeuwijk, Carel F.W. Peeters, Olivia Zumsteg, Ioannis N. Athanasiadis, George van Voorn doi.o [bsky, 2 points, 0 comments]

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