Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression

In order to effectively evaluate the diet nutritional value of dairy cows,it is essential to accurately predict the diet nutrients digestibility(DND).Conventional predicting DND methods are usually based on the least squares linear regression analysis(LS-LRA),which often relies on a large amount of training samples to accomplish reliable predictions.However,in real-world applications,it is often extremely difficult,costly and time-consuming to obtain a large number of measured samples,especially for the DND prediction of dairy cows.This paper applies a Gaussian process regression(GPR)technique to predict the DND indicators of dairy cows in small samples.To evaluate prediction accuracy effectively,we compared the GPR technique with the LS-LRA,radial basis function artificial neural network(RBF-ANN),support vector regression(SVR)and least squares support vector regression(LS-SVR)methods,using the required sample data obtained from actual digestion experiments.The prediction results indicate that the GPR technique is superior to other conventional methods(especially the LS-LRA method)in predicting the main DND indicators of dairy cows such as dry matter digestibility(DMD),organic matter digestibility(OMD),neutral detergent fiber(NDFD),acid detergent fiber(ADFD),and crude protein digestibility(CPD).It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples,which is often the case in the prediction of DND indicators of dairy cows,and then more coincide with actual needs.