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A bi-stage data-driven process-based model for sorghum breeding and yield prediction: coupling explainable artificial

Zheng Ni1, Yanbin Chang1, Joshua Kemp2

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

Frontiers in Plant Science
|January 26, 2026
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Summary

This study introduces an explainable hybrid crop model for sorghum breeding. It uses data-driven and process-based methods to predict yield and identify elite hybrids, enhancing agricultural efficiency and sustainability.

Keywords:
GxEcrop modelingdata-drivenexplainable AIneural networkprocess-based

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Area of Science:

  • Agricultural Science
  • Computational Biology
  • Plant Breeding

Background:

  • Global population growth necessitates advanced breeding methods for increased food supply.
  • Sorghum is a vital cereal crop with diverse types (Grain, Forage, Dual Purpose, Photoperiod-Sensitive), requiring tailored breeding strategies.
  • Genotype x Environment (GxE) interactions significantly impact crop performance, demanding sophisticated modeling approaches.

Purpose of the Study:

  • To develop a bi-stage, data-driven and process-based crop model for sorghum breeding.
  • To provide breeding recommendations by analyzing Genotype x Environment (GxE) effects.
  • To enhance the interpretability and flexibility of crop models through explainable AI methods.

Main Methods:

  • Integrated a process-based crop model with explainable data-driven techniques.
  • Utilized seven years of hourly weather data, soil factors, management practices, and parental information from 651 males and 131 females.
  • Predicted hourly dry weight (leaves, stems, grain) and final yield, incorporating management practices.

Main Results:

  • Achieved a combined Relative Root Mean Squared Error of 16%-19% across various environmental conditions, demonstrating robust predictive accuracy.
  • Successfully identified elite sorghum hybrids across four distinct types, reducing the need for extensive field trials.
  • Revealed significant variability in GxE interactions, underscoring the importance of environment-specific breeding strategies.

Conclusions:

  • The explainable hybrid model framework significantly improves crop modeling and plant breeding.
  • This approach enhances agricultural efficiency and sustainability by optimizing breeding recommendations.
  • Tailored breeding strategies based on GxE analysis are crucial for maximizing crop performance.