Pecking activity detection in group-housed turkeys using acoustic data and a deep learning technique

Visual monitoring of behaviour on-farm is mostly challenging due to the number of animals to be observed and the time required. However, behavioural problems such as cannibalism in turkeys may be preceded by subtle changes in behaviour. Machine learning techniques allow automatic behavioural monitoring of livestock to be carried out under different farming conditions. The aim of this study was to develop and test a novel pecking activity detection tool for potential use on turkey farms by means of acoustic data and a convolutional neural networks (CNN) model. Under near to commercial conditions, two metallic balls were used as pecking objects and suspended from the ceiling. Each pecking object was equipped with a microphone connected via a cable to a top view camera positioned on the ceiling. The recorded sound data were sampled in slots of 1 s and high pass filtering was performed to eliminate background noises. A total of 9200 filtered sound files were used for training and validation, and 3900 for testing set. They were labelled manually as peck or non-peck, using 7360 (80%) for training and 1840 (20%) for validation files, and fed into the CNN model. An additional 3900 new filtered sound clips were used to test the detection phase of the trained model. The experimental results illustrate that the deep learning-based detection method achieved high overall accuracy, precision, recall and F1-score of 96.8, 89.6, 92.0 and 90.8% in the detection phase. This indicates that the proposed technique could be used as a precise method for the detection of pecking activity levels in turkeys.