Automated oestrous detection in sows using a robotic imaging system

Accurate oestrous detection is critical to optimise sows' reproductive performance. The conventional method of oestrous detection relies on the laborious back-pressure test. This study presents an automated oestrous detection method for sows housed in individual stalls using a robotic imaging system and neural networks. A robotic imaging system consisting of a LiDAR camera was used to monitor a group of stall-housed sows at a 10-min interval to capture their postures and vulva volume. Imagery data were analysed using a previously developed pipeline. Results showed that significant changes were observed in daily standing index, sternal lying index, lateral lying index, posture change frequency, and vulva volume before the onset of oestrous. A 1-D convolutional neural network model architecture for oestrous detection was developed using Days from Weaning (DFW), behaviour features, and vulva volume features as inputs. The oestrous detection models were evaluated using 10-fold cross validation. The training and testing accuracies of the oestrous detection model were 96.1 ± 2.0% and 92.3 ± 10.1% when using the DFW and behaviour features as input. The model's training and testing accuracies increased to 98.1 ± 2.4% and 98.0 ± 4.2% when vulva volume features were added to the input. While it is difficult to trace the behaviour of sows housed in group conditions, combining vulva volume features with DFW could be a suitable method to detect the onset of oestrous in these sows. The training and testing accuracies of this method of oestrous detection were 97.9 ± 1.4% and 95.2 ± 4.8%. However, further validation under real group house conditions is needed.