Automated behavioural response assessment to a feeding event in two heritage chicken breeds

Fraess, Garrett A.; Bench, C. J.; Tierney, Keith B.

doi:10.1016/j.applanim.2016.03.002

Cited by 15 publications

(22 citation statements)

References 26 publications

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“…The technological advancements help poultry farmers to implement good poultry welfare by implementing one of the monitoring and tracking systems to observe the sickness or well-being of the poultry chickens. The location of feeders and drinkers can be examined and changed with the presence of clusters or groups of the poultry chickens [16]. The activity such as moving, perching, eating, and drinking may help in understanding the health of the poultry chicken [17,18].…”

Section: Related Workmentioning

confidence: 99%

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

et al. 2021

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The poultry industry contributes majorly to the food industry. The demand for poultry chickens raises across the world quality concerns of the poultry chickens. The quality measures in the poultry industry contribute towards the production and supply of their eggs and their meat. With the increasing demand for poultry meat, the precautionary measures towards the well-being of the chickens raises the concerns of the industry stakeholders. The modern technological advancements help the poultry industry in monitoring and tracking the health of poultry chicken. These advancements include the identification of the chickens’ sickness and well-being using video surveillance, voice observations, ans feces examinations by using IoT-based wearable sensing devices such as accelerometers and gyro devices. These motion-sensing devices are placed over a chicken and transmit the chicken’s movement data to the cloud for further analysis. Analyzing such data and providing more accurate predictions about chicken health is a challenging issue. In this paper, an IoT based predictive service framework for the early detection of diseases in poultry chicken is proposed. The proposed study contributes by extending the dataset through generating the synthetic data using Generative Adversarial Networks (GAN). The experimental results classify the sick and healthy chicken in a poultry farms using machine learning classification modeling on the synthetic data and the real dataset. Theoretical analysis and experimental results show that the proposed system has achieved an accuracy of 97%. Moreover, the accuracy of the different classification models are compared in the proposed study to provide more accurate and best performing classification technique. The proposed study is mainly focused on proposing an Industrial IoT-based predictive service framework that can classify poultry chickens more accurately in real time.

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Section: Related Workmentioning

confidence: 99%

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

et al. 2021

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“…Through the number of changed pixels between two adjacent frames divided by the overall number of pixels in the image, the flock activity level can be calculated (Youssef et al, 2015;Fraess et al, 2016). The method is heavily influenced by plumage differences.…”

Section: Flock Activity Analysismentioning

confidence: 99%

“…The method is heavily influenced by plumage differences. Group activity around the food trough was assessed by the percent pixel change (Fraess et al, 2016). The study showed that the method could identify changes in activity level, identify abnormal activity away from the feeder and serve as an early health warning system.…”

Section: Flock Activity Analysismentioning

confidence: 99%

“…Monitoring the resource use of chickens can improve the housing design and allocate resources optimally (Ringgenberg et al, 2015;Li et al, 2017). Through the location of chicken clustering, the quality of house facilities (e.g., feeders, drinkers) may be examined (Fraess et al, 2016). Detecting the activity of poultry, such as eating, drinking, moving and perching, may estimate whether they are healthy and therefore provide early warning of diseases (Colles et al, 2016;Dawkins et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming

Ren

et al. 2020

Animal

129

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There is increasing public concern about poultry welfare; the quality of animal welfare is closely related to the quality of livestock products and the health of consumers. Good animal welfare promotes the healthy growth of poultry, which can reduce the disease rate and improve the production quality and capacity. As behaviour responses are an important expression of welfare, the study of behaviour is a simple and non-invasive method to assess animal welfare. The use of modern technology offers the possibility to monitor the behaviour of broilers and laying hens in a continuous and automated way. This paper reviews the latest technologies used for monitoring the behaviour of broilers and laying hens under both experimental conditions and commercial applications and discusses the potential of developing a precision livestock farming (PLF) system. The techniques that are presented and discussed include sound analysis, which can be an online tool to automatically monitor poultry behaviour non-invasively at the group level; wireless, wearable sensors with radio-frequency identification devices, which can automatically identify individual chickens, track the location and movement of individuals in real time and quantify some behavioural traits accordingly and image processing technology, which can be considered a direct tool for measuring behaviours, especially activity behaviours and disease early warning. All of these technologies can monitor and analyse poultry behaviour, at the group level or individual level, on commercial farms. However, the popularity and adoption of these technologies has been hampered by the logistics of applying them to thousands and tens of thousands of birds on commercial farms. This review discusses the advantages and disadvantages of these techniques in commercial applications and presents evidence that they provide potential tools to automatically monitor the behaviours of broilers and laying hens on commercial farms. However, there still has a long way to go to develop a PLF system to detect and predict abnormal situations.

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“…Поведение, обеспечивающее комфорт (engaging in comfort behavior, other maintenance behavior) (54,110), важно для поддержания хорошей кондиции и перьевого покрова. Оно включает в себя потягивания, взмахи крыльями, покачивания тела.…”

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ПОВЕДЕНЧЕСКИЕ РЕАКЦИИ И БЛАГОПОЛУЧИЕ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ПТИЦЫ (Обзор)

СУЛИМОВА¹

2020

s-h. biol.

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Обеспечение благополучия птицы и производство высококачественной продукции остаются актуальными проблемами как в мировом птицеводстве, так и в России в связи с необходимостью повышения конкурентоспособности отрасли (Welfare Quality® Assessment for poultry, 2009; I.J.H. Duncan, 1981; J.A. Mench, 1992). Особое внимание при улучшении благополучия животных придается соответствию между их биологическими особенностями и технологиями животноводства (D.A. Orlov c соавт., 2016). На благополучие сельскохозяйственной птицы влияют болезни, стрессы, питание, условия содержания (D.C. Jr Lay c соавт., 2011). Цель настоящего обзора -анализ нарушений поведения птицы в связи с ее неблагополучием, а также рассмотрение поведенческих предпочтений в качестве условий обеспечения благополучия. Птица в определенных пределах способна адаптироваться к различным условиям окружающей среды (M. Brantsaeter c соавт., 2018). Неспособность к адаптации выражается в изменениях физиологического статуса, а также в нарушениях поведения, которые могут нанести вред и животным, и обслуживающему персоналу. Сильные проявления страха, такие как паника или резкие попытки побега, повышают энергозатраты и могут привести к повреждениям или даже смерти (S. Waiblinger c соавт., 2006). Страх как нежелательное эмоциональное состояние снижает общую активность животных. Регулярные отрицательные раздражители тормозят социальные взаимодействия (J.A. Mench, 2004; B. Forkman c соавт., 2007). Боязнь человека влияет на благополучие и продуктивность (T. Kutzer c соавт., 2015; M.A. Sutherland c соавт., 2012; F. Barone c соавт., 2018). Отношения человека и животного включают в себя визуальное, тактильное, обонятельное и групповое восприятие (S. Waiblinger c соавт., 2006). Птица чувствительна к визуальному контакту с человеком, но некоторые взаимодействия, такие как перемещение человеком руки на боковую часть клетки или приближение на короткое время, могут снизить ее пугливость (J.A. Mench, 2004). Оценка реакции животных позволяет сделать выводы о том, как они воспринимают всех людей или конкретного человека. Это зависит от вида животного и системы содержания, от характера его взаимодействий с человеком (позитивные, нейтральные или негативные), от качества ухода (S. Waiblinger c соавт., 2006). Понимание поведения -важный аспект концепции благополучия птицы (В.Н. Тихонов c соавт., 2008). С точки зрения обеспечения благополучия животных особо выделяют возможность проявлять природное поведение и наличие элементов в обстановке, приближающих ее к природной среде (Animal Welfare Issues Compendium; D. Fraser, 2008). Домашние куры сохранили значительную часть свойственных диким формам поведенческих потребностей (M.S. Dawkins, 1988). Главные из них -гнездование, пищевое и питьевое поведение, двигательная и комфортная активность, социальные взаимодействия (I.J.H. Duncan, 1998; T. Shimmura c соавт., 2018). Ограничение естественного поведения ведет к ухудшению благополучия птицы. Факторы окружающей среды, такие как высокая интенсивность света и скученность, также c высокой вероятностью пр...

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Automated behavioural response assessment to a feeding event in two heritage chicken breeds

Cited by 15 publications

References 26 publications

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

An Approach towards IoT-Based Predictive Service for Early Detection of Diseases in Poultry Chickens

Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming

ПОВЕДЕНЧЕСКИЕ РЕАКЦИИ И БЛАГОПОЛУЧИЕ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ПТИЦЫ (Обзор)

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