A Broiler Stress Detection System Based on Audio Signal Processing

Jakovljević, Nikša; Maljković, Nina; Miskovic, Dragisa; Knežević, Petar; Delić, Vlado

doi:10.1109/telfor48224.2019.8971336

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Carroll et al [5] detected gurgling noises, a distinct symptom of respiratory diseases in poultry, also using MFCCs and obtained a 73.4% accuracy value and a 51.4% recall value. Jakovljević et al [9] detected stress on broiler farms during the first four weeks of life, and the accuracy of the classification varied between 63% and 83%, depending on the animal age.…”

Section: Discussionmentioning

confidence: 99%

“…New bioacoustic precision livestock farming (PLF) techniques offer the possibility of automatically tracking and monitoring the behavior of poultry through its calls [6]. Some applications of bioacoustic PLF techniques in chickens are as follows: the identification of acoustic parameters for estimating the welfare of broiler chickens [7], the automatic detection of flapping, purring, coughing, and crowing with 60 acoustic classification features [8], stress detection in broiler chickens using acoustic features for voice quality assessment and vocal emotion recognition [9], the detection of symptoms of respiratory diseases in poultry through the cepstral coefficient of mel frequency [5], and the acoustic features of the vocalization signal in poultry to identify and monitor bronchitis and Newcastle disease [10].…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Ginovart-Panisello,

Iriondo,

Panisello Monjo

et al. 2024

Applied Sciences

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Acoustic studies on poultry show that chicken vocalizations can be a real-time indicator of the health conditions of the birds and can improve animal welfare and farm management. In this study, hens vaccinated against infectious laryngotracheitis (ILT) were acoustically recorded for 3 days before vaccine administration (pre-reaction period) and also from vaccination onwards, with the first 5 days being identified as the “reaction period” and the 5 following days as “post reaction”. The raw audio was pre-processed to isolate hen calls and the 13 Mel-frequency cepstral coefficients; then, the spectral centroid and the number of vocalizations were extracted to build the acoustic dataset. The experiment was carried out on the same farm but in two different houses. The hens from one house were assigned to the control group, without administration of the anti-inflammatory product, and the other formed the treatment group. Both acoustic data sets were recorded and processed in the same way. The control group was used to acoustically model the animal reaction to the vaccine and we automatically detected the hens’ vaccine reactions and side effects through acoustics. From Scikit-Learn algorithms, Gaussian Naive Bayes was the best performing model, with a balanced accuracy of 80% for modeling the reactions and non-reactions caused by ILT in the control group. Furthermore, the importance of algorithm permutation highlighted that the centroid and MFCC4 were the most important features in acoustically detecting the ILT vaccine reaction. The fitted Gaussian Naive Bayes model allowed us to evaluate the treatment group to determine if the vocalizations after vaccine administration were detected as non-reactions, due to the anti-inflammatory product’s effectiveness. Of the sample, 99% of vocalizations were classified as non-reactions, due to the anti-inflammatory properties of the product, which reduced vaccine reactions and side effects. The non-invasive detection of hens’ responses to vaccination to prevent respiratory problems in hens described in this paper is an innovative method of measuring and detecting avian welfare.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Ginovart-Panisello,

Iriondo,

Panisello Monjo

et al. 2024

Applied Sciences

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“…Estas características se estudiaron sobre datos de audio guardados por una empresa avícola donde un veterinario realizó el etiquetado para eventos como el estrés causado por la alta temperatura de la habitación, captura y transferencia de las aves y el mantenimiento regular del lugar, teniendo como resultado una precisión entre el 63.0% y 83.0% en la clasificación, dependiendo de la edad de las aves y analizando todas las características juntas [39].…”

Section: Análisis Del Sonidounclassified

Ganadería de precisión, una revisión a los avances dentro de la avicultura enfocados a la crianza de pollos de engorde.

Juárez

2023

Prisma

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La producción avícola es una de las industrias con mejor desarrollo dentro del sistema agropecuario panameño, sin embargo, para los próximos años se espera un crecimiento en la demanda de alimentos causado por el crecimiento de la población. Esto ha planteado soluciones como fomentar la aparición de un mayor número de productores y potenciar la producción animal intensiva, ante esta problemática ha surgido un nuevo campo de investigación denominado Ganadería de precisión (PLF), este es definido como la capacidad de monitorizar y de dar seguimiento en tiempo real al bienestar, producción, reproducción, impacto ambiental y salud del ganado, empleando nuevas tecnologías en Inteligencia artificial, automatización, internet de las cosas y sistemas de información. Este artículo tiene por objetivo ser una revisión sobre los fundamentos de la ganadería de precisión en la crianza de pollos de engorde, reuniendo trabajos de actualidad y sus tendencias de trabajo, desde bases de investigación bibliográficas, con miras a la adopción de este campo en los proyectos futuros dentro de Panamá. Como resultado de esta revisión se encontró que los países europeos como Bélgica, Países Bajos, Reino Unido e Italia tienen la mayor cantidad de investigadores y trabajos relacionados con esta rama, siendo proyectos basados en sensores, machine learning, visión artificial y análisis del sonido las actuales tendencias de investigación, también se encontró que aún se discuten dilemas éticos relacionados con el cuidado y bienestar animal dentro de este campo.

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“…A valuable benefit of this method was the improvement of animal welfare and poultry production through automatic monitoring [35]. Jakovljević et al [36] introduced an audio-based system to monitor broiler chicken stress. It was based on audio signals from the first few weeks in a chicken's life to detect stress.…”

Section:  Issn: 2502-4752mentioning

confidence: 99%

Poultry disease early detection methods using deep learning technology

Yajie,

Md Johar,

Iqbal Hajamydeen

2023

IJEECS

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<span>Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.</span>

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A Broiler Stress Detection System Based on Audio Signal Processing

Cited by 10 publications

References 6 publications

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Ganadería de precisión, una revisión a los avances dentro de la avicultura enfocados a la crianza de pollos de engorde.

Poultry disease early detection methods using deep learning technology

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