Field test of algorithm for automatic cough detection in pig houses

Guarino, Marcella; Jans, Peter; Costa, Annamaria; Aerts, Jean‐Marie; Berckmans, Daniël

doi:10.1016/j.compag.2007.08.016

Cited by 59 publications

(36 citation statements)

References 11 publications

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“…However, the main difference between the present study and the reported literature (Chung et al, 2013;Guarino et al, 2008; is the ratio of number of cough sounds to total number of sounds. The ratio in these studies was between 60% and 26.9% while in the present study it was only 6.3%.…”

Section: Algorithm Performance Indicatorscontrasting

confidence: 83%

“…The algorithm attained a sensitivity of 57%, 51% and 43%, for houses 1, 2 and 3, respectively, which is lower than values reported for algorithms used to detect pig coughs (Chung et al, 2013;Guarino et al, 2008;. These authors reported sensitivities ranging from 85% to 94% (Table 4).…”

Section: Algorithm Performance Indicatorsmentioning

confidence: 71%

“…A cough sound monitor has previously been used for the detection of respiratory disease in pigs (Van Hirtum et al, 1999;. Further studies reported the use of an online, cough monitoring system (Exadaktylos et al, 2008;Guarino et al, 2008), demonstrated that coughing sources could be localised and that the cough monitor could be used for the detection of respiratory diseases in pigs (Finger et al, 2014). However, this technology has not been evaluated to date for calves, although it has been shown that it is possible to differentiate between mechanical and calf cough sounds (Ferrari et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Vandermeulen

Bahr

Johnston

et al. 2016

Computers and Electronics in Agriculture

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a b s t r a c tBovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/ anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

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Section: Algorithm Performance Indicatorscontrasting

confidence: 83%

Section: Algorithm Performance Indicatorsmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

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Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Vandermeulen

Bahr

Johnston

et al. 2016

Computers and Electronics in Agriculture

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“…And then we divided the number characters into symmetrical type (0,1,3,8) and non-symmetrical type (2,4,5,6,7,9) and assumed the eigenvectors of the numbers affixed to pigs to be identified as G LM (P). If they satisfied one of the following expressions, the numbers to be identified were symmetric, otherwise, they were non-symmetric.…”

Section: L[ho %Orfnmentioning

confidence: 99%

Automated detection of sick pigs based on machine vision

Zhu

et al. 2009

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems

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“…Many sensors and sensing techniques are available or under development to increase the ability of automating measurements of animal behavioral and biological responses. Nowadays, image analysis (Ahrendt et al, 2011), sound analysis (Guarino et al, 2008) and other electronic sensors such as RFID (Brown-Brandl et al, 2013) are increasingly finding their use in animal production.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of lactating sow behaviors through depth image processing

Lao

Brown–Brandl

Stinn³

et al. 2016

Computers and Electronics in Agriculture

105

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Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow's behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow's behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow's behavioral images. This tool is conducive to investigating behavioral responses and time budget of lactating sows and their litters to farrowing crate designs and management practices. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. b s t r a c tManual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow's behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow's behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow's behavioral images. This tool is conducive to investigating behavioral responses an...

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Field test of algorithm for automatic cough detection in pig houses

Cited by 59 publications

References 11 publications

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Automated detection of sick pigs based on machine vision

Automatic recognition of lactating sow behaviors through depth image processing

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