Predicting some Milk Constituents from Linear Body Measurements and Udder Characteristics of Bunaji Cows Utilizing Machine Learning Techniques

Abstract

Machine Learning techniques are capable of being used in agriculture since they are powerful, fast and flexible tools for classification and predictions of traits of economic importance, particularly those involving nonlinear systems. This study aimed to show how well machine learning methods could predict milk's crude protein and fat content based on the Bunaji cows' linear body measurements and udder characteristics. Forty (40) lactating Bunaji cows from two dairy farms in Yola South Local Government Area, Adamawa State, were purposefully selected for the study. Milk samples were collected from the cows (one-off measurement). The crude protein and fat content of the milk were determined in percentage. An ensemble model consisting of the random forest (RF), support vector machine (SVM) and neural network (NN) algorithms were created using the Orange Software. The models were cross-validated using five folds to assess their robustness. Mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2 ) are the metrics utilized for the evaluation. Using the udder features, the approach was used to construct demonstrative models for the prediction of milk protein content. The SVM and RF learners had MSE that was lower than that of NN, showing that the two were superior to NN. The lower performance of NN in this study can be explained on the basis of extremely low data that was available for this study. RF learners exhibited a low likelihood of mistakes in predicting crude protein from udder traits despite the study's small data set. Large data sets were needed for machine learning techniques. As a result, these models must be improved with lots of data.