Physiology-Informed Sparse Identification of Dynamic Crop Models

Abstract

Developing resilient crops requires models that accurately capture how plant growth dynamics emerge from the continuous interplay between genetic and environmental factors. While high-throughput phenotyping platforms generate data for dynamic growth models, current modelling approaches show limitations: process-based crop growth models (CGMs) capture environmental dynamics but are difficult to scale across many genotypes because they require calibration of many parameters, while quantitative genetic models allow modelling many genotypes but often summarise time-varying environments using static covariates. To address this gap, we propose a data-driven framework to identify reduced systems of ordinary differential equations (ODEs) describing crop growth dynamics as functions of environmental variables and a controllable number of genotype-specific coefficients (GSCs). Using the Sparse Identification of Nonlinear Dynamics (SINDy) method, we infer model structure directly from data via sparse regression on a physiologically informed library of candidate functions. In this framework, GSCs parameterise cultivar-specific differences in response to environmental conditions. The resulting models are linear in these GSCs, making them compatible with mixed-model and genomic prediction approaches. We evaluate the approach on an in silico dataset generated with WOFOST for five potato cultivars across twelve growing seasons. Our identified ODEs generalise to held-out years and achieve R² > 0.9 for end-of-season yield prediction across held-out years when compared to full-scale WOFOST simulations. These results provide a proof of concept for SINDy-based identification of parsimonious dynamic crop models from longitudinal crop-growth data.

Replacing EEG with peripheral physiological signals for driving behavior recognition: a physiology-informed multimodal approach
Huaqiang Xu, Yuwei Zhang, Xiaoqing Sun +2
Cognitive Neurodynamics27 Oct 2026Peer-reviewedLicence not statedFull textNeurosciencePMC
Geometry-informed temperature band screening for crop microclimate management in a commercial tomato greenhouse
Xiaojing Liu, Jianlin Li, Hongsheng Lin +1
Frontiers in plant science16 Oct 2026Peer-reviewedCC BYFull textPlant biologyEurope PMC
Identification of Antioxidant Plant Extracts and Garcinia amplexicaulis Metabolites with Ferroptosis Inhibitor-like Chemical Properties for Alternative Crop Protection
Auriane Leblond, Guillaume Viault, Thomas Charpentier +7
Metabolites25 Sep 2026Peer-reviewedCC BYFull textEcologyHALEurope PMC
Physiology-Informed Blink Artefact Detection from Bilateral Frontal EEG Under Asymmetric Interference
Weiqiang Zhang, Menglong Li, Yujie Zhang +5
Bioengineering (Basel, Switzerland)1 Oct 2026Peer-reviewedCC BYFull textNeuroscienceEurope PMCDOAJ
Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation
Xu Wang, Feng Xiao, Zejun Li +5
Plant Physiology2 Jul 2026Peer-reviewedCC BY-NC-NDPlant biologyHAL

Cite

BibTeX RIS

BibTeX

@misc{verburg2026physiologyinformed,
  title = {{Physiology-Informed Sparse Identification of Dynamic Crop Models}},
  author = {Corné Verburg and Alexander Heinlein and Fred van Eeuwijk and Neil Budko},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.18920},
  eprint = {2610.18920},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.18920},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Physiology-Informed Sparse Identification of Dynamic Crop Models
AU  - Corné Verburg
AU  - Alexander Heinlein
AU  - Fred van Eeuwijk
AU  - Neil Budko
PY  - 2026
DA  - 2026/10/12
PB  - arXiv
DO  - 10.48550/arxiv.2610.18920
UR  - https://arxiv.org/abs/2610.18920
AB  - Developing resilient crops requires models that accurately capture how plant growth dynamics emerge from the continuous interplay between genetic and environmental factors. While high-throughput phenotyping platforms generate data for dynamic growth models, current modelling approaches show limitations: process-based crop growth models (CGMs) capture environmental dynamics but are difficult to scale across many genotypes because they require calibration of many parameters, while quantitative genetic models allow modelling many genotypes but often summarise time-varying environments using static covariates. To address this gap, we propose a data-driven framework to identify reduced systems of ordinary differential equations (ODEs) describing crop growth dynamics as functions of environmental variables and a controllable number of genotype-specific coefficients (GSCs). Using the Sparse Identification of Nonlinear Dynamics (SINDy) method, we infer model structure directly from data via sparse regression on a physiologically informed library of candidate functions. In this framework, GSCs parameterise cultivar-specific differences in response to environmental conditions. The resulting models are linear in these GSCs, making them compatible with mixed-model and genomic prediction approaches. We evaluate the approach on an in silico dataset generated with WOFOST for five potato cultivars across twelve growing seasons. Our identified ODEs generalise to held-out years and achieve R^2 > 0.9 for end-of-season yield prediction across held-out years when compared to full-scale WOFOST simulations. These results provide a proof of concept for SINDy-based identification of parsimonious dynamic crop models from longitudinal crop-growth data.
ER  -

Abstract

Related

Cite