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Updated: Jun 23, 2026

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Prediction of Digestible and Metabolizable Energy in Swine Feed Using Machine Learning.

Jun-Wen Yu1, Shang-Hua Liu1, Dong-Xin Ye1

  • 1School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.

ACS Omega
|June 22, 2026
PubMed
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Accurately predicting swine feed energy content is vital. AutoGluon, an automated machine learning framework, accurately estimated digestible energy and metabolizable energy, outperforming other models for precision feed formulation.

Area of Science:

  • Animal Science
  • Machine Learning
  • Agricultural Technology

Background:

  • Accurate prediction of feed energy content is essential for optimizing swine diets and improving production efficiency in animal husbandry.
  • Traditional methods may lack the precision required for modern, data-driven swine nutrition.

Purpose of the Study:

  • To apply AutoGluon, an automated machine learning framework, for predicting digestible energy and metabolizable energy in swine feed.
  • To compare the performance of AutoGluon against artificial neural networks and support vector machines for feed energy prediction.
  • To develop a practical tool for rapid and precise estimation of feed energy content.

Main Methods:

  • Utilized a dataset comprising 1341 records from the China feed database and 45 records for pregnant sows.

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  • Applied AutoGluon, artificial neural networks, and support vector machines to predict digestible and metabolizable energy.
  • Evaluated model performance using R-squared values on general and pregnant sow test datasets.

    • Main Results:

    • AutoGluon demonstrated superior accuracy on the general test set, achieving R-squared values of 0.940 for digestible energy and 0.938 for metabolizable energy.
    • For pregnant sows, AutoGluon achieved the best performance for metabolizable energy prediction (R-squared = 0.939).
    • Artificial neural networks showed a slight advantage in predicting digestible energy for pregnant sows (R-squared = 0.920).

    Conclusions:

    • AutoGluon offers a highly accurate and efficient method for predicting swine feed energy content.
    • The developed AutoGluon-based 'pig energy predictor' facilitates precision feed formulation and data-driven decision-making in animal husbandry.
    • This approach supports the optimization of swine nutrition and production efficiency through reliable energy estimation.