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Related Experiment Video

Updated: Jan 9, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

A data-driven crop model for biomass sorghum growth process simulation.

Yanbin Chang¹, Zheng Ni¹, Juan S Panelo^2,3

¹School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.

Frontiers in Plant Science

|December 1, 2025

Summary

This summary is machine-generated.

This study presents a new data-driven crop model for accurate biomass sorghum yield prediction. The model effectively simulates crop growth, distinguishing environmental and management impacts for precision agriculture.

Keywords:

biomass sorghum data-driven crop model integrated crop model process-based crop model yield prediction

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Area of Science:

Agricultural Science
Plant Physiology
Computational Biology

Background:

Accurate crop yield prediction is vital for resource management, especially in variable climates.
Biomass sorghum growth simulation requires models that account for complex genotype-environment-management interactions.

Purpose of the Study:

To develop a novel data-driven crop model for simulating phenotypic changes in biomass sorghum.
To improve the accuracy of biomass sorghum growth and yield predictions.
To disentangle the effects of environmental and management factors on crop development.

Main Methods:

Integration of a detailed physiological sorghum development framework.
Application of data-driven techniques for genotypic parameter calibration using experimental data.
Simulation of phenotypic changes influenced by genotype, environment, and management.

Main Results:

The model accurately predicts biomass production in sorghum.
The model successfully differentiates the impacts of environmental and management factors on phenotype.
Effective model calibration was achieved even with limited experimental data.

Conclusions:

The developed model enhances the accuracy and applicability of biomass sorghum prediction.
The model provides valuable insights for optimizing precision agriculture strategies.
This approach offers a robust method for simulating crop growth under varying conditions.