Dynamic weighing algorithm for dairy cows based on time domain features and error compensation

•The hidden information in the dynamic weighing signal of cows is quantified using time domain features.•There is a correlation between dimensionless time-domain features and the prediction error of EWT.•The influence of hidden information on EWT prediction results can be weakened by using error compensation.•This method improves the accuracy of dynamic weighing in complex environments. Weight is an essential indicator for the growth and development of cows. Traditional static weighing method requires a lot of labor and financial resources, the existing dynamic weighing algorithm has no consideration of the influence of hidden information on weighing results during cows' movement, and fails to meet the weighing requirements in complex environments. In this paper, we propose a cow dynamic weighing algorithm based on time domain features and error compensation, quantitatively analyze the hidden information in the dynamic weighing signal, and compensate for the error of the preliminary prediction results, leading to a high-precision dynamic weighing of cows. Firstly, the dynamic weighing signal of cows is pre-processed to obtain the effective signal, and each time domain feature of the effective signal are extracted. Then, the effective signal is decomposed by using the empirical wavelet transform (EWT), and the trend component's mean value is taken as the prediction value of EWT, so that the error between the prediction weight value and the real weight can be calculated. Next, four time-domain features are used as input and the error values are used as output to construct the data set, and the sparrow search algorithm (SSA) is used to obtain the optimal parameters of support vector regression (SVR) model for the training. Finally, the error prediction values output from the model are summed with the prediction values of EWT as the final weight prediction value. To evaluate the performance of the method in this paper, we collect dynamic weighing data from 626 cows, and then randomly select 501 data as the training set while the remaining 125 data as the test set. The average relative error of weight prediction of our method is 0.68%, and the root mean square error is 4.83 kg, better than the existing dynamic weighing algorithms. The experimental results show that the method in this paper is feasible to quantitatively analyze the implicit information of the effective signal according to the time domain features, and the error compensation method can effectivel