Detection of jumping and landing force in laying hens using wireless wearable sensors

Banerjee, Debasmit; Daigle, Courtney L; Dong, Bo; Wurtz, K.E.; Newberry, Ruth C.; Siegford, Janice M.; Biswas, Subir

doi:10.3382/ps.2014-04006

Cited by 34 publications

(21 citation statements)

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“…This has led to calls for a more integrated approach for 'reality mining' of these data sets and for more cross-fertilisation of ideas between disciplines [17]. An example of this more integrated approach is a recent study by Banerjee et al [18], who developed a method to detect jumps in laying hens based on some of the key features that are used to estimate forces during human vertical jumps [19].…”

Section: Introductionmentioning

confidence: 99%

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

et al. 2015

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Background: Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Results: Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines.

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Section: Introductionmentioning

confidence: 99%

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

et al. 2015

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“…Another technology that has been tested to automatically detect laying hens is based on sensors that allow the building of a geographic information system (GIS) [21,22,23]. These sensors have to be worn by hens.…”

Section: Introductionmentioning

confidence: 99%

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology

Zaninelli

Redaelli²,

Tirloni

et al. 2016

Sensors

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The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, as final target, a possible reduction of atmospheric ammonia levels and bacterial load. Tests were carried out for three weeks and involved 7 ISA (Institut de Sélection Animale) brown laying hens. The first 5 days was used to set up the detection sensor, while the other 15 days were used to evaluate the accuracy of the resulting monitoring system, in terms of sensitivity and specificity. The setup procedure included the evaluation of different color background (CB) thresholds, used to discriminate the information contents of the thermographic images. At the end of this procedure, a CB threshold equal to an increase of 3 °C from the floor temperature was chosen, and a cutoff level of 196 colored pixels was identified as the threshold to use to classify a positive case. The results of field tests showed that the developed monitoring system reached a fine detection accuracy (sensitivity = 97.9% and specificity = 94.9%) and the IR technology proved to be a possible solution for the development of a detection sensor necessary to reach the scope of this study.

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“…Regarding the value of technological advances to address specific poultry welfare issues, research has shown that piezoelectric sensors [52] and kinematic technology [85] can be useful to investigate locomotion characteristics and gait abnormalities in broilers, while wireless acceleration sensors can be used to determine the effect of height on the incidence of bone breakage in laying hens [55]. Even though these examples of technological developments are still at an experimental phase and would need further research for commercial implementation, these approaches can be helpful to understand bird locomotion characteristics and to detect locomotion abnormalities, at least in experimental studies.…”

Section: Discussionmentioning

confidence: 99%

“…A pressure mapping system and a video camera were installed as a validation method to measure the landing forces and to record the jumps. Banerjee et al [55] reported an average landing force of 15.85 and 20.8 KJ when jumping from heights of 41 to 61 cm, respectively. This technological approach capable of assessing the landing forces on laying hens differing in age, size, and plumage integrity may enable the improvement of housing and perching design [55], which is important to resolve some persistent issues on aviary systems that are relevant for the welfare of laying hens.…”

Section: Sensorsmentioning

confidence: 99%

Technology and Poultry Welfare

Sassi

Averós

Estévez

2016

Animals

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Consideration of animal welfare is essential to address the consumers’ demands and for the long term sustainability of commercial poultry. However, assessing welfare in large poultry flocks, to be able to detect potential welfare risks and to control or minimize its impact is difficult. Current developments in technology and mathematical modelling open new possibilities for real-time automatic monitoring of animal welfare and health. New technological innovations potentially adaptable to commercial poultry are appearing, although their practical implementation is still being defined. In this paper, we review the latest technological developments with potential to be applied to poultry welfare, especially for broiler chickens and laying hens. Some of the examples that are presented and discussed include the following: sensors for farm environmental monitoring, movement, or physiological parameters; imaging technologies such as optical flow to detect gait problems and feather pecking; infrared technologies to evaluate birds’ thermoregulatory features and metabolism changes, that may be indicative of welfare, health and management problems. All these technologies have the potential to be implemented at the commercial level to improve birds’ welfare and to optimize flock management, therefore, improving the efficiency of the system in terms of use of resources and, thus, long term sustainability.

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Detection of jumping and landing force in laying hens using wireless wearable sensors

Cited by 34 publications

References 10 publications

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology

Technology and Poultry Welfare

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