Using machine learning regression models to predict the pellet quality of pelleted feeds

Abstract

Pelleted feeds are widely used in monogastric animal production systems because they not only improve animal performance (increasing digestibility and feed consumption) but are convenient to store and handle. However, pellet quality can be affected by many factors. While previous studies have reported the effect of a single or several factors on pellet quality, no studies have investigated how pellet quality can be affected by the large number of factors that vary during feed manufacturing. Therefore, the current study reports using machine learning regression models to predict pellet quality using commercial feed mill data. A dataset consisting of 2471 observations describing the pellet manufacturing process, the feed formulation, and environmental conditions (e.g. outdoor temperature) were collected from two feed mill lines for 8 months. Sixteen features (13 continuous, 3 categorical) were used for building the regression models, and the output was the pellet durability index (PDI) of the pelleted feeds. Twelve regression algorithms including Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, Ridge Regression (RR), Support Vector Regression (SVR), Linear Support Vector Regression (LSVR), Random Forest (RF), Decision Tree (DT), Gradient Boosting Regression (GBR), Adaptive Boosting Regression (ABR), Multi-Layer Perceptron (MLP) neural network, K-Nearest Neighbor (KNN), and Stacking Regression (SR) were examined in this study. Feature importance analysis using permutation importance was performed to identify what features were most relevant for each model. Average outdoor temperature, bakery byproduct and wheat inclusion levels, as well as production line all had high permutation importance values, while the fat added into the mixer (with controls at the mill already in place to limit it) was less important than most features. The cleaned dataset was preprocessed and then split into a training (80 % of total samples, n = 1147) and a testing (20 % of total samples, n = 287) set. A 5-fold cross-validation process was applied and learning curves were used to verify the presence of overfitting for each algorithm before and after tuning the hyperparameters on the training set. The models that exhibited overfitting were excluded from the final results and only models with tuned hyperparameters were evaluated on the testing set. The SVR algorithm was selected as the best overall model for predicting PDI, as it had the lowest mean absolute and mean squared prediction errors (MAE = 3.280, MSPE = 16.192), and the second highest concordance correlation coefficient (CCC = 0.636). In conclusion, this study shows that feed mill features describing manufacturing parameters, feed formulation, and environmental data can be successfully used to build machine learning regression models for pellet quality prediction.