China Animal Husbandry & Veterinary Medicine ›› 2025, Vol. 52 ›› Issue (8): 3753-3766.doi: 10.16431/j.cnki.1671-7236.2025.08.024

• Genetics and Breeding • Previous Articles    

Identification and Correlation Analysis of Ewe Licking and Standing Posture in Newborn Lambs

WANG Guoshan1,2, SONG Yifeng1,2, AN Xiaoping1,2, WANG Yuan1,2, LIU Na1,2, WANG Wenwen1,2, QI Jingwei1,2   

  1. 1. College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China;
    2. Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, National Center of Technology Innovation for Dairy-Breeding and Production Research Center, Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Hohhot 010018, China
  • Received:2024-10-16 Published:2025-08-02

Abstract: 【Objective】 The intelligent detection of ewes licking lambs and the standing postures of lambs,the characteristics of the lamb standing process as well as the influence of ewes licking lambs on the standing of lambs were intended to be studied in this experiment. 【Method】 Videos of the licking behavior of 30 ewes and the standing process of lambs were collected to train the YOLOv5s model.20 multiparous single-fetus Saanen dairy goats and their newborn lambs were selected.Those with the licking time of ewes less than 120 s were divided into control group (Con group),and those with the licking time of ewes more than 120 s were divided into licking group (Lick group),with 10 in each group.The licking behavior of ewes and the standing postures of lambs were recognized based on the trained YOLOv5s algorithm.The characteristics of the standing process of lambs were analyzed by relying on the Farneback optical flow algorithm.The time and frequency of the ewes’ licking behavior and the lambs’ standing between consecutive frames were extracted by combining the two algorithms.Pearson correlation analysis was adopted,and One-Way ANOVA was used to explore the effect of lamb licking on lamb standing. 【Result】 ①The average precision of the YOLOv5s algorithm for identifying the licking posture was 97.90%.The average precision for identifying the lying posture of lambs was 90.70%.The average precision for identifying the posture of lambs attempting to stand was 82.60%.And the average precision for identifying the standing posture of lambs was 86.30%.②Through the analysis of the characteristics of the lamb standing process by the Farneback optical flow algorithm,the motion trajectory and direction changes of lambs during the process from lying down to standing could be effectively demonstrated.③The longest licking time was 1 462.00 s,the shortest was 0 s,with an average of 410.10 s.The time when lambs first attempted to stand ranged from 284.00 to 1 195.00 s,with an average of 529.25 s.The time when lambs first successfully stood ranged from 695.00 to 2 921.00 s,with an average of 1 464.35 s.The time for attempting to stand ranged from 323.00 to 1 785.00 s,with an average of 930.10 s.The minimum number of times that lambs attempted to stand was 11.00,the maximum was 46.00,with an average of 23.60 times.④The number of attempts of lambs in Con group to stand was significantly higher than that of lambs in Lick group.The duration of each attempt to stand by lambs in both Con and the Lick groups showed an increasing tendency as the number of attempts increased.⑤The total duration of ewes licking lambs was significantly negatively correlated with the duration of lambs’ attempts to stand,the duration of lambs’ first successful standing and the frequency of lambs’ attempts to stand (P<0.05).⑥There was no significant difference in the time from birth to the first attempt to stand between lambs in Lick group and those in Con group (P>0.05).The time for lambs in Lick group to attempt to stand was significantly shorter than that of lambs in Con group (P<0.05).The time for lambs in Lick group to first successfully stand was significantly shorter than that of lambs in Con group (P<0.05).The number of attempts of lambs in Lick group to stand was significantly less than that of lambs in Con group (P<0.05). 【Conclusion】 The YOLOv5s model could accurately identify the postures of ewes licking lambs and the standing postures of lambs,and the Farneback optical flow algorithm could well analyze the characteristics of the lamb standing process.Moreover,the longer the licking time of ewes was,the higher the vitality of lambs would be,which could help lambs successfully stand up as soon as possible.

Key words: YOLOv5s model; Farneback optical flow algorithm; ewe licking; lamb standing

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