Browsing Animal & Grassland Research & Innovation Programme by Author "Carpentier, Lenn"
Automatic cough detection for bovine respiratory disease in a calf houseCarpentier, Lenn; Berckmans, Daniel; Youssef, Ali; Berckmans, Dries; van Waterschoot, Toon; Johnston, Dayle; Ferguson, Natasha; Earley, Bernadette; Fontana, Ilaria; Tullo, Emanuela; et al. (Elsevier, 2018-07-06)In calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown.
An ethogram of biter and bitten pigs during an ear biting event: first step in the development of a Precision Livestock Farming toolDiana, Alessia; Boyle, Laura; Carpentier, Lenn; Piette, Deborah; Berckmans, Daniel; Norton, Tomas; Teagasc Walsh Fellowship Programme; KU Leuven internal research grant; European Union; Reference 6497; et al. (Elsevier, 2019-03-22)Pigs reared in intensive farming systems are more likely to develop damaging behaviours such as tail and ear biting (EB) due to their difficulty in coping with the environment and their inability to perform natural behaviours. However, much less is known about the aetiology of EB behaviour compared to tail biting behaviour. Application of new intervention strategies may be the key to deal with this welfare issue. The discipline of Precision Livestock Farming (PLF) allows farmers to improve their management practices with the use of advanced technologies. Exploring the behaviour is the first step to identify reliable indicators for the development of such a tool. Therefore, the aim of this study was to develop an ethogram of biter and bitten pigs during an EB event and to find potential features for the development of a tool that can monitor EB events automatically and continuously. The observational study was carried out on a 300 sow farrow-to-finish commercial farm in Ireland (Co. Cork) during the first and second weaner stages. Three pens per stage holding c. 35 pigs each, six pens in total, were video recorded and 2.2 h of videos per pen were selected for video analysis. Two ethograms were developed, one for the biter and one for the bitten pig, to describe their behavioural repertoire. Behaviours were audio-visually labelled using ELAN and afterwards the resulting labels were processed using MATLAB® 2014. For the video data, duration and frequency of the observed behavioural interactions were quantified. Six behaviours were identified for the biter pig and a total of 710 interactions were observed: chewing (215 cases), quick bite (138 cases), pulling ear (97 cases), shaking head (11 cases), gentle manipulation (129 cases) and attempt to EB (93 cases). When the behaviour observed was not certain, it was classified as doubt (27 cases). Seven behaviours were identified for the bitten pig in response to the biters behaviour and were divided in: four non-vocal behaviours described as biting (40 cases), head knocking (209 cases), shaking/moving head (225 cases) or moving away (156 cases); and three vocal behaviours identified as scream (74 cases), grunt (166 cases), and squeal (125 cases). Vocal behaviours were classified using a verified set of features yielding a precision of 83.2%. A significant difference in duration was found between all the behaviours (P < 0.001), except between gentle manipulation and chewing where no difference in duration was found (P < 0.338). The results illustrate the heterogeneity of EB behaviours, which may be used to better understand this poorly studied damaging behaviour. They also indicate potential for the development of a PLF tool to automatically, continuously monitor such behaviour on farm by combining the behaviour of the biter pig and the bitten pigs responses.