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.
For objective evaluation, monitoring and management of poultry welfare, novel non-invasive indicators, proxies, and iceberg indicators of stress is required. Using thermal imaging and video imaging, we explored animal-based indicators such as eye blinks and facial temperatures of chicks as measures of stress. Routinely collected video data from poultry commercial farms has the potential to be digitized by Artificial Intelligence and the combination of indicators of stress be analyzed in the development of welfare assessment protocols. Monitoring facial temperature variations via infrared imaging and blinking frequency of chickens could be a sensitive metric that may offer insights on the stressful environments. The identification of relevant poultry welfare indicators to improve welfare assessment such as the iceberg indicators are essential to monitor welfare at both small scale and large-scale industrial poultry production facilities. In commercial farming systems, chicks are reared without a mother. This absence of maternal influence can cause welfare problems when the chicks become older. Chicks imprint on their mothers they are young, and this mediates their stress and fear response. It is important to recognise problems early in the development of chicks to avoid welfare issues when they are older. One way to assess welfare is by measuring affective states. Research has shown chickens can display empathy, both towards their offspring and towards conspecifics. Measures of negative and positive affective states, either behavioural or physiological, could be good welfare indicators. This study employed non-invasive methods to measure affective states in laying hen chicks. Using video and thermal imaging, it analysed temperature changes in the peripheral areas and head region as well as changes in blinking behaviour before and after exposure to a stressor. The prediction was that the temperature would decrease in the eye and peripheral regions in response to a stressor and that the blinking rate would decrease. These changes would be indicative of a negative affective state. The results showed that the eye temperature as well as the blinking rate both decreased, whereas the temperature in the head region and the beak area increased. These results could be indicative of a negative affective state.
Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.
Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.
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