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Related Experiment Video

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A new prediction model based on deep learning for pig house environment.

Zhidong Wu1,2,3, Kaixiang Xu4, Yanwei Chen4

  • 1School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China. wzd139446@163.com.

Scientific Reports
|December 27, 2024
PubMed
Summary
This summary is machine-generated.

A novel Bayesian optimization (BO) enhanced Squeeze-and-Excitation Convolutional Neural Network (SE-CNN) with Gated Recurrent Unit (GRU) accurately predicts pig house environments. This model improves animal welfare through precise environmental control, outperforming existing methods.

Keywords:
Bayesian optimization algorithmConvolutional neural networkEnvironmental prediction modelGated recurrent unitPig houseSqueeze and excitation

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Area of Science:

  • Agricultural Engineering
  • Artificial Intelligence in Agriculture
  • Environmental Monitoring

Background:

  • Accurate prediction of indoor pig house environmental parameters (temperature, humidity, CO2, NH3) is crucial for animal welfare and efficient farm management.
  • Existing prediction models often struggle with complex environmental dynamics and achieving high prediction accuracy.

Purpose of the Study:

  • To develop an advanced prediction model for pig house environments using a combination of Bayesian optimization (BO), Squeeze-and-Excitation Convolutional Neural Network (SE-CNN), and Gated Recurrent Unit (GRU).
  • To enhance prediction accuracy and stability for key environmental parameters, thereby supporting proactive environmental control and improving animal welfare.

Main Methods:

  • Proposed a hybrid model integrating BO for hyper-parameter tuning, SE-CNN for feature extraction, and GRU for sequence modeling.
  • The SE-CNN block extracts local features, with the SE block optimizing feature channel weights for improved discrimination.
  • GRU captures long-term dependencies in the environmental data sequence for future value prediction.

Main Results:

  • The BO-SE-CNN-GRU model demonstrated superior performance in predicting temperature, humidity, CO2, and NH3 concentrations compared to CNN-LSTM, CNN-BiLSTM, and CNN-GRU models.
  • Achieved high prediction accuracy, evidenced by a coefficient of determination (R²) of 0.9883, mean absolute error (MSE) of 0.03243, and mean absolute percentage error (MAPE) of 0.01536 for ammonia prediction.
  • The model exhibited significant advantages in prediction accuracy and stability, providing reliable decision support.

Conclusions:

  • The developed BO-SE-CNN-GRU model offers a highly accurate and stable solution for predicting indoor pig house environments.
  • This advanced prediction capability enables timely intervention and control measures, significantly contributing to improved animal welfare and optimized farm management.
  • The model's effectiveness highlights the potential of integrating advanced AI techniques for precision livestock farming.