文献类型： 外文期刊

作者： Wu, Zhidong ¹ ; Xu, Kaixiang ¹ ; Chen, Yanwei ¹ ; Liu, Yonglan ¹ ; Song, Wusheng ¹ ;

作者机构： 1.Qiqihar Univ, Sch Mech & Elect Engn, Qiqihar 161006, Peoples R China

2.Engn Technol Res Ctr Precis Mfg Equipment & Ind Pe, Qiqihar 161006, Peoples R China

3.Heilongjiang Acad Agr Sci, Harbin 150000, Peoples R China

关键词： Pig house; Bayesian optimization algorithm; Convolutional neural network; Gated recurrent unit; Squeeze and excitation; Environmental prediction model

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.9； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations. The SE block further learns the weights of the feature channels, highlights the important features and suppresses the unimportant ones, improving the feature discrimination ability. The extracted local features are fed into the GRU network to capture the long-term dependency in the sequence, and this information is used to predict future values. The indoor environmental parameters of the pig house are predicted. The prediction performance is evaluated through comparative experiments. The model outperforms other models (e.g., CNN-LSTM, CNN-BiLSTM and CNN-GRU) in predicting temperature, humidity, CO2 and NH3 concentrations. It has higher coefficient of determination (R2), lower mean absolute error (MSE), and mean absolute percentage error (MAPE), especially in the prediction of ammonia, which reaches R2 of 0. 9883, MSE of 0.03243, and MAPE of 0.01536. These data demonstrate the significant advantages of the BO-SE-CNN-GRU model in prediction accuracy and stability. This model provides decision support for environmental control of pig houses.