Using Acceleration Data to Automatically Detect the Onset of Farrowing in Sows

被引:27
作者
Traulsen, Imke [1 ]
Scheel, Christoph [2 ]
Auer, Wolfgang [3 ]
Burfeind, Onno [4 ]
Krieter, Joachim [2 ]
机构
[1] Georg August Univ, Dept Anim Sci, Livestock Syst Grp, Albrecht Thaer Weg 3, D-37075 Gottingen, Germany
[2] Univ Kiel, Inst Anim Breeding & Husb, Olshausenstr 40, D-24098 Kiel, Germany
[3] MKW Elect GmbH, Jutogasse 3, A-4675 Weibern, Austria
[4] Chamber Agr Schleswig Holstein, D-24327 Blekendorf, Germany
关键词
farrowing; acceleration measurement; CUSUM control chart; management assistance; ear sensor; BEHAVIOR; PREDICTION; SPACE; GILTS;
D O I
10.3390/s18010170
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1) Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2) Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3) Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation) combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4) Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates.
引用
收藏
页数:13
相关论文
共 45 条
[41]   Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data [J].
Ng, Johan Y. Y. ;
Zhang, Joni H. ;
Hui, Stanley S. ;
Jiang, Guanxian ;
Yau, Fung ;
Cheng, James ;
Ha, Amy S. .
PLOS ONE, 2024, 19 (03)
[42]   Analysis of harsh braking and harsh acceleration occurrence via explainable imbalanced machine learning using high-resolution smartphone telematics and traffic data [J].
Ziakopoulos, Apostolos .
ACCIDENT ANALYSIS AND PREVENTION, 2024, 207
[43]   Predicting new-onset post-stroke depression from real-world data using machine learning algorithm [J].
Chen, Yu-Ming ;
Chen, Po-Cheng ;
Lin, Wei-Che ;
Hung, Kuo-Chuan ;
Chen, Yang-Chieh Brian ;
Hung, Chi-Fa ;
Wang, Liang-Jen ;
Wu, Ching-Nung ;
Hsu, Chih-Wei ;
Kao, Hung-Yu .
FRONTIERS IN PSYCHIATRY, 2023, 14
[44]   Development of a pressure-, velocity-, and acceleration-dependent phenomenological friction model using experimental data of sliding tests between 11 polymers and stainless steel [J].
Tapia, Nicolas F. ;
Reyes, Sergio I. ;
Vassiliou, Michalis F. ;
Almazan, Jose L. .
ENGINEERING STRUCTURES, 2024, 318
[45]   Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study [J].
Sun, Chengkun ;
Mobley, Erin ;
Quillen, Michael ;
Parker, Max ;
Daly, Meghan ;
Wang, Rui ;
Awad, Ziad ;
Fishe, Jennifer ;
Parker, Alexander ;
George, Thomas ;
Bian, Jiang ;
Xu, Jie .
JMIR CANCER, 2025, 11