Posture identification for stall-housed sows around estrus using a robotic imaging system

Abstract

Posture patterns of sows or gilts could reflect their welfare, estrus, and health status. However, due to the large animal-to-staff ratio at sow farms, there is not yet a solution to closely monitor individual sows’ posture patterns to determine their estrus status and make other managemental decisions. This study aimed to evaluate the performance of the convolutional neural network (CNN) models in recognizing sows’ posture patterns using images of a LiDAR camera. The camera equipped on a wall-mounted robotic platform was used to take images of 21 sows from the back view at a 5-minute interval. Four types of images (IR: infrared image, DI: depth image, PCI: point cloud image, PCI_IR: point cloud image with IR as the alpha channel) were used to identify the postures of individual sows. Five CNN models were evaluated in this study, including SP_CNN (5 layers), VGG16, MobileNet, Xception, and DenseNet121. Results showed that the image types had no significant impact on the validation accuracy of posture recognition (99.8 ± 0.2%, p > 0.1). Statistical results indicate that VGG16 required significantly more time (t = 2,218 ms) than the other four models in processing single images without significantly improving identification accuracy. The SP_CNN model had a significantly lower validation accuracy (99.6%) and used significantly less image processing time (t = 91 ms) than the rest of the models. It was found that for SP_CNN, DI images outperformed the other three types of images. Although no significant difference was found in validation accuracy (99.91% ± 0.04%) among MobileNet, Xception, and Densnet121. The MobileNet required significantly less time to process single images (t = 210 ms). The Xception model with PCI_IR images showed the highest test accuracy (98.6%). The results also show that both hourly and daily activity levels (standing or sitting) were significantly higher on the day of the onset of estrus than the values on the previous days. Meanwhile, the daily idle level (lateral lying) decreased significantly from two days prior to the onset of estrus. The decrease in daily idle level was drastic on the day of onset of estrus (12.9 ± 7.1%) and before (15.0 ± 12.0%). The study also found that monitoring intervals should not exceed 15 min for accurately quantifying sows’ activity levels. The study concluded that posture patterns derived from the robotic imaging system could help detect sows’ estrus status.