Smart modelling of dairy milk production with machine learning

Abstract

Complex non-linear correlations between the predictors (features) and the independent variable can be modeled using machine learning techniques. This modeling strategy is clever because it uses machine learning to develop process predictions after the right model has been created. Therefore, the purpose of this work was to examine how to simulate dairy milk production using machine learning. The daily milk output in Iwo and its environs between May 26, 2021, and May 31, 2022, as measured in liters, was used. Five features in the data set were identified; the day of the week, month, year, season and day number. We tested a total of 14 different supervised learning (regression) machine learning techniques. 20% of the data were used for validation, while 80% were used to train these algorithms. The Bagged Tree gave the highest R - square value of 0.67 and the lowest RMSE of 20.26 among the 14 Machine Learning techniques taken into account. It is therefore recommended to be used in smart prediction of daily milk production in Iwo and its environs. Also, season was found to influence milk production in the study area with higher milk production in wet season than dry season(p<.05).