Acoustic features of vocalization signal in poultry health monitoring

Mahdavian, Alireza; Minaei, Saeid; Marchetto, Peter; Almasganj, Farshad; Rahimi, Shaban; Yang, Ce

doi:10.1016/j.apacoust.2020.107756

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…Rizwan et al 100 have used an SVM-based classifier to detect rale sounds generated by chickens from the vocalization of chickens. Mahdavian et al 98 have used SVM to identify the birds infected by bronchitis and Newcastle disease. Cinkler et al 108 have used SVM-based two-step classifiers to identify the sounds of birds on agricultural land and play a sound when birds are identified near the crops.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…WET provides a multiscale representation of signal's complexity by analyzing it in different time-frequency scales. 98 Various statistical measures can be derived from the entropy distribution vector E(a,b) to form feature representa- tions. These measures may include mean, standard deviation, skewness, kurtosis, or other statistical descriptors.…”

Section: Vocalization Analysis Process For Birds' Health Managementmentioning

confidence: 99%

“…Wavelet entropy transformation (WET) is also a feature extraction technique that combines the concepts of wavelet analysis and entropy to characterize a signal complexity and information content. WET provides a multiscale representation of signal’s complexity by analyzing it in different time-frequency scales . Various statistical measures can be derived from the entropy distribution vector E ( a , b ) to form feature representations.…”

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

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A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds' health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer's income increases, and high-quality food products are available to people at low cost.

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Section: Data Analysis Methodsmentioning

confidence: 99%

Section: Vocalization Analysis Process For Birds' Health Managementmentioning

confidence: 99%

Section: Role Of Emerging Technologies In Poultry Health Managementmentioning

confidence: 99%

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A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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“…Avian influenza Imaging (thermal images) experimental setting [29] Campylobacter jejuni imaging (flock movement-optical flow) dataset from broiler buildings [30] Clostridium perfringens sound analysis (vocalizations) experimental setting [31] Coccidiosis sensor (volatile organic compounds) experimental setting + broiler building [32] Coccidiosis sensor (volatile organic compounds) dataset from broiler buildings [33] Coccidiosis + Salmonella spp. imaging (feces) dataset of images [20] Ektoparasites wearable sensor (activity) dataset from poultry building [34] Infectious bronchitis sound analysis (rales) experimental setting [35] Infectious bronchitis sound analysis (rales) experimental setting [36] Infectious bronchitis + Newcastle disease sound analysis (vocalizations) experimental setting [37] Newcastle disease sound analysis (sneezes) experimental setting [38] Newcastle disease imaging (posture and mobility) experimental setting [39] Newcastle disease sound analysis (vocalizations) experimental setting [40] Non-specific, clinical signs imaging (feces) dataset of images [41] Non-specific, clinical signs imaging and sound analysis dataset of audio samples [42] Non-specific, clinical signs imaging (feces) dataset from broiler building [43] Non-specific, clinical signs imaging (head motion, appearance) experimental setting [44] Non-specific, clinical signs imaging (posture, appearance) experimental setting [45] Non-specific, clinical signs sound analysis (abnormal respiratory sounds) dataset from broiler building [46] Non-specific, clinical signs imaging (posture, appearance) dataset of images [47] Pasteurella spp. imaging (thermal images) experimental setting [48] * All studies were perfomed with chickens, except Noh et al [29] who also used ducks.…”

Section: Disease/pathogen Methods (Variables Measured) Type Of Study ...mentioning

confidence: 99%

Diagnosing Infectious Diseases in Poultry Requires a Holistic Approach: A Review

Liebhart

Bilić

Grafl

et al. 2023

Poultry

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Controlling infectious diseases is vital for poultry health and diagnostic methods are an indispensable feature to resolve disease etiologies and the impact of infectious agents on the host. Although the basic principles of disease diagnostics have not changed, the spectrum of poultry diseases constantly expanded, with the identification of new pathogens and improved knowledge on epidemiology and disease pathogenesis. In parallel, new technologies have been devised to identify and characterize infectious agents, but classical methods remain crucial, especially the isolation of pathogens and their further characterization in functional assays and studies. This review aims to highlight certain aspects of diagnosing infectious poultry pathogens, from the farm via the diagnostic laboratory and back, in order to close the circle. By this, the current knowledge will be summarized and future developments will be discussed in the context of applied state-of-the-art techniques. Overall, a common challenge is the increasing demand for infrastructure, skills and expertise. Divided into separate chapters, reflecting different disciplines, daily work implies the need to closely link technologies and human expertise in order to improve bird health, the production economy and to implement future intervention strategies for disease prevention.

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“…Birds are very vocal animals and use their voice for many purposes. The acoustics of birds have been used to detect diseases in animals (Mahdavian et al, 2021). Furthermore, specific types of calls have been used to identify the difference between feather pecking and non-feather pecking flocks (Bright, 2008).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Effect of an Acute Stressor in Laying Hens using Thermographic Imaging and Vocalisations

Heuvel

Graat

Youssef

et al. 2022

Preprint

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The laying hen sector has multiple issues concerning the animals welfare. One crucial factor negatively impacts chicken welfare is stress. The conventional way of measuring and assessing chicken stress is time-consuming and subjective to the assessor. On the other hand, sensing and sensor technologies can be used to obtain objective, continuous and non-invasive/contactless measures of animal behavioural and physiological welfare indicators. The present study aims to investigate the use of thermographic imaging and microphones (sound) in obtaining objective indicators for acute stress in laying hens. During this study, 40 laying hens were stressed by opening an umbrella as a stressor starting from one day age until nine-weeks old. The birds were stressed every other day. Another 12 birds were housed in another cage. These birds were not stressed and served as a control group. The surface temperatures of the birds comb and beak decreased (1 C and 2.5 C respectively) in response to the applied stressor. This effect was only seen in the treatment group and not in the control birds. The number of vocalisations the birds produced significantly decreased shortly after stress. The number of calls in the stressed group decreased from 39.5 to 12.1 calls/minute, where the control group decreased from 27.8 to 22.5 calls/minute. It was hypothesized that the number of vocalisations would increase after stress. This difference could be due to the daily behavioural rhythm performed by the birds. The birds might naturally produce more calls at certain hours of the day because of certain behaviours they perform. Three different neural network algorithms were employed to differentiate between the vocalisation of stressed and the control group. This was done by converting the audio files to images and feeding them to the pretrained convolutional neural networks (CNN). The Resnet CNN had the highest categorising accuracy with an overall accuracy of 86 percent. The changes in surface temperature of the beak, comb, eye, and head, as well as the results from the audio analysis could serve as potential indicator for acute stress in laying hens. Future research is warranted to validate the methodologies and findings under different environmental conditions and stressors.

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Acoustic features of vocalization signal in poultry health monitoring

Cited by 31 publications

References 23 publications

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Diagnosing Infectious Diseases in Poultry Requires a Holistic Approach: A Review

Quantifying the Effect of an Acute Stressor in Laying Hens using Thermographic Imaging and Vocalisations

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