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Prediction of the diet energy digestion using kernel extreme learning machine: A case study with Holstein dry cows

文献类型： 外文期刊

作者： Fu, Qiang 1 ; Shen, Weizheng 1 ; Wei, Xiaoli 1 ; Zhang, Yonggen 3 ; Xin, Hangshu 3 ; Su, Zhongbin 1 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northeast Agr Univ, Coll Elect & Informat, Harbin 150030, Peoples R China

2.Minist Agr, Key Lab Pig Breeding Facil Engn, Harbin 150030, Peoples R China

3.Northeast Agr Univ, Coll Anim Sci & Technol, Harbin 150030, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Dairy cow diet; Digestible energy prediction; Energy digestibility prediction; Non-parametric model; Kernel extreme learning machine

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2020 年 169 卷

页码：

收录情况： SCI

摘要： In order to effectively evaluate the diet nutritional and feeding value, it is essential to accurately predict the indicators of diet energy digestion of dairy cows, thus further optimizing feed formulation and improving feeding management. Traditional mathematical models used to predict the main indicators of diet energy digestion of dairy cows are usually based on linear regression (LR) method. However, as a typical parametric model, the LR-based method is limited by regression function assumption, and the inaccurate assumed function sometimes leads to the learned regression model biased from the ground-truth one. In this study, we propose a kernel extreme learning machine (KELM) technique to predict the indicators of Holstein dry cows' diet digestible energy (DE) and energy digestibility (ED). KELM is a typical non-parametric machine learning model, which does not require any specific assumptions about the regression function in advance. The learned modal by KELM can well fit the actual one only by learning the training sample data in most cases. To evaluate prediction accuracy effectively, we compared the KELM technique with traditional parametric prediction LR model and other commonly used non-parametric models such as radial basis function artificial neural network, support vector machine and standard extreme learning machine methods. The required sample data is obtained from actual feeding and digestion experiments. The prediction results indicate that the proposed KELM-based prediction technique is superior to other methods in most performance metrics for prediction of the DE and ED indicators of diet energy digestion of Holstein dry cows. In particular, it has higher prediction accuracy than traditional LR-based prediction method, and can predict DE and ED better under small samples conditions. The developed KELM-based prediction model may be utilized to provide key decision support information to animal nutrition experts, livestock farmers and feed suppliers. The presented model is also exploited as a potential tool for accurate evaluation of dairy cows diet feeding effect.

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