Prediction of growth in grower-finisher pigs using recurrent neural networks

Abstract

Accurate prediction of pig growth is an important tool for supporting sustainable global pork production, due to the potential benefits to farm management in monitoring growth according to target, promoting efficient use of resources and planning sale of finished animals. In this paper, a long short-term memory (LSTM) recurrent neural network architecture was designed to predict how long it would take for grower-finisher pigs housed in a climate-controlled building reflecting commercial practice to reach a target liveweight of 90 kg. To make these predictions, data on pig liveweight and food consumption were integrated, along with various combinations of environmental temperature recorded by the building control system and, to represent seasonality, encodings of day of the year. Also, ensembles were created and evaluated by averaging the predictions of multiple models, each trained with different combinations of input features, and comparisons were made between the performance of the LSTM models and other machine learning algorithms. Overall, the LSTM model achieved the best prediction performance, with a RMSE of 2.470 days and a Pearson's correlation coefficient of 0.962. Integrating both environmental temperature and seasonality data into the models generally resulted in an improvement in predictive performance. The results demonstrate the potential of LSTM models as a precision livestock farming tool for the early prediction of growth in commercial finisher pigs. • A machine learning pipeline for the forecasting of pig growth was designed. • Predicting the time to reach a target weight can improve data availability. • Specialised neural network architectures are very effective at predicting growth. • Utilising environmental temperatures can improve growth prediction accuracy. • Specialised algorithms can be used to remove outliers from non-cyclical time series.