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Related Experiment Video

Updated: Aug 11, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Machine learning versus crop growth models: an ally, not a rival.

Ningyi Zhang1, Xiaohan Zhou1,2, Mengzhen Kang3

  • 1Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University, PO Box 16, 6700 AA Wageningen, The Netherlands.

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|February 8, 2023
PubMed
Summary
This summary is machine-generated.

Combining process-based models (PBMs) and machine learning (ML) creates knowledge- and data-driven models (KDDM) for accurate, interpretable agricultural yield prediction. This approach addresses global food security challenges amid climate change.

Keywords:
KnowledgeMachine learningProcessand databased modelsdriven modellingyield prediction

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Area of Science:

  • Agricultural Science
  • Computational Biology
  • Environmental Science

Background:

  • Global population growth and climate change necessitate sustainable food production.
  • Process-based models (PBMs) and machine learning (ML) are key tools in agricultural research, each with limitations.
  • Current crop models face challenges in prediction accuracy and interpretability.

Purpose of the Study:

  • To develop a combined knowledge- and data-driven modeling (KDDM) approach for agricultural applications.
  • To enhance prediction accuracy and interpretability in crop yield prediction.
  • To explore the potential of KDDM for upscaling and downscaling crop models.

Main Methods:

  • Integration of PBMs and ML using parallel, serial, or modular structures.
  • Utilizing sensor data for simplified model parameterization.
  • Developing KDDM for improved yield prediction and phenotyping.

Main Results:

  • KDDM achieves high prediction accuracy and interpretability.
  • The approach simplifies model parameterization through sensor data integration.
  • KDDM enhances the scalability of crop models from gene to global levels.

Conclusions:

  • KDDM offers a promising synergy between simulation models and data science in agriculture.
  • This approach is vital for addressing food security, climate change, and sustainability.
  • KDDM advances agricultural modeling despite ongoing research into complex genetic and physiological processes.