Detecting symptoms of diseases in poultry through audio signal processing

Carroll, Brandon; Anderson, David V.; Daley, Wayne; Harbert, Simeon D.; Britton, Douglas F.; Jackwood, Mark W.

doi:10.1109/globalsip.2014.7032298

Cited by 31 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noise is usually the result of the sound of vehicles and has been shown to have a negative effect on animal foraging [113]. Researchers [25]; decision tree [26] support vector machine [123]; decision tree [124] example based classifier [143] difference in time of arrival [135] feed forward artificial neural network [95]; linear model [56] difference in time of arrival [14] no Figure 1. A decision tree to help researchers identify bioacoustics studies relevant to animal disease status, location detection, physiological information, number of animals and species detection.…”

Section: Anthropogenic Noisementioning

confidence: 99%

“…Labels and vectors were used to train a support vector machine, which learned to distinguish between the healthy and unhealthy flocks. Another algorithm detected rales by labelling audio recordings of spectrograms from 8 min of audio recordings collected over 25 days of continuous recordings [124]. They then extracted MFCC vectors, clustered them in order to examine their distribution over a window of time, and classified the features using a decision tree.…”

Section: Chickensmentioning

confidence: 99%

See 1 more Smart Citation

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Mcloughlin

Stewart

McElligott

2019

J. R. Soc. Interface.

106

View full text Add to dashboard Cite

Vocalizations carry emotional, physiological and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation and sound scene classification. These methods are already used to automatically classify animal vocalizations, for example, in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation and sound classification. Following this, we detail the vocalizations produced by three of the most important farm livestock species: chickens ( Gallus gallus domesticus ), pigs ( Sus scrofa domesticus ) and cattle ( Bos taurus ). Finally, we describe how these methods can be applied to monitor animal welfare with new potential for developing automated methods for large-scale farming.

show abstract

Section: Anthropogenic Noisementioning

confidence: 99%

Section: Chickensmentioning

confidence: 99%

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Mcloughlin

Stewart

McElligott

2019

J. R. Soc. Interface.

106

View full text Add to dashboard Cite

show abstract

“…Then, using these five features a neural network was applied to detect healthy or infected chickens and was able to differentiate at an accuracy level of 66.6 and 100% on day 2 and day 8 post-infection, respectively. Additionally, by recording chickens infected with infectious bronchitis virus (IBV), and training a computer algorithm with manually labeled recordings, IBV infected chickens can be detected based on vocalization ( 65 ). The algorithm used in the aforementioned study was trained to recognize rales, which are commonly produced from IBV infected chickens, and was able to detect increased rale frequency days before clinical signs of disease were evident.…”

Section: Part Imentioning

confidence: 99%

Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

et al. 2018

View full text Add to dashboard Cite

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched.

show abstract

“…Despite research highlighting the potential for automated monitoring of vocalizations as a means to assess and monitor animal welfare states [6], progress has been slow. In chickens, most methods have focused on detecting issues associated with respiratory diseases or measuring growth [7][8][9][10]. However, due to the links between emotional states and types of vocalizations and recent advances in machine learning applied to audio data [3,[11][12][13][14][15], we hypothesized that automated detection of chicken distress calls would be feasible.…”

Section: Introductionmentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an ‘iceberg indicator’ of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

show abstract

Detecting symptoms of diseases in poultry through audio signal processing

Cited by 31 publications

References 11 publications

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Automated bioacoustics: methods in ecology and conservation and their potential for animal welfare monitoring

Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

Automated identification of chicken distress vocalizations using deep learning models

Contact Info

Product

Resources

About