Automatic recognition of lactating sow postures from depth images by deep learning detector

Abstract The behaviors of livestock on farms are the primary representatives of animal welfare, health conditions and social interactions. Measuring behavior quantitatively in an automatic detection system on computer vision provides valuable behavioral information in an efficient and noninvasive way compared with manual observations or sensing techniques. Lactating sow postures, which are the crucial indicator of maternal evaluation, provide fundamental information for studying the maternal behavioral characteristics and regularities. We introduce a detector, Faster R-CNN, on deep learning framework to identify five postures (standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency) and obtain sows accurate location in loose pens. The detection system consists of a Kinect v2 sensor that acquires depth images and a program that identifies sow postures and locates its bounding-boxes. The depth images of testing dataset of a sow were acquired at 5 frames per second in 24 h on the 15th day of postpartum, and training dataset were collected by some different sows. Since the identification performance from RGB images are impacted by the color and illumination variations caused by in-situ heat lamp and day-night cycle, we show that the automatic detection from depth images could avoid disturbances of the light. We find that the sow spent greater amount of time in recumbency (92.9% at night and 84.1% during the daytime) as compared with standing (0.4% at night and 10.5% during the daytime) and sitting (0.55% at night and 3.4% during the daytime). Statistically, the sow’s activity level is non-uniform in 24-h of a day, and her preferred lying positions is accordant with the pen’s floor design. The posture’s change frequency and average duration are presented. From the estimated general manner of posture change, we find that the sow takes more time in descending body than ascending, which could be a favorable indication of maternal ability with a slow-motion falling to avoid crushing piglets.