Development of an early warning system for a broiler house using computer vision

Kashiha, Mohammadamin; Pluk, Arno; Bahr, Claudia; Vranken, Erik; Berckmans, Daniël

doi:10.1016/j.biosystemseng.2013.06.004

Cited by 81 publications

(64 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…data-based mechanistic modelling, in which models are determined from the data alone but are then interpreted on the basis of biological understanding of the system (Young, 1993). By using this technique, it may be possible to control the activity of chickens by predicting (modelling) their dynamic responses to changes in an input stimulus, such as step-wise variations in light intensity (Aerts, Berckmans, Saevels, Decuypere, & Buyse, 2000;Kashiha, Pluk, Bahr, Vranken, & Berckmans, 2013). However, before implementing a control system at farm level, it is vital to determine a suitable target for activity, based upon the behaviour and welfare of broilers.…”

Section: Introductionmentioning

confidence: 98%

Towards real-time control of chicken activity in a ventilated chamber

Youssef

Exadaktylos

Berckmans

2015

Biosystems Engineering

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

Towards real-time control of chicken activity in a ventilated chamber

Youssef

Exadaktylos

Berckmans

2015

Biosystems Engineering

View full text Add to dashboard Cite

“…An estimate of the number of pigs in the pen was derived using the segmentation as a base. The direct and continuous tracking of the number of pigs in different parts of the pen could be an important feature to monitor and controlling the heating system, based on observed pig behaviour, as it has been earlier suggested by Shao and Xin (2008) and for the use of an early warning system for poultry (Kashiha et al, 2013). A further development could be to identify individual pigs with altered behaviour, or to identify their location in the box, which may indicate inferior health or acute illness.…”

Section: Resultsmentioning

confidence: 99%

“…A further development could be to identify individual pigs with altered behaviour, or to identify their location in the box, which may indicate inferior health or acute illness. In earlier studies (Shao and Xin, 2008;Kashiha et al, 2013; Two cases considered whole sequence and sequence with border cases omitted since they are ambiguous for human inspection. Oczak et al, 2013), segmentation presumed the flooring being uniform due to the lack of bedding material that compromized the earlier segmentation technologies.…”

Section: Resultsmentioning

confidence: 99%

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique

Nilsson

Herlin

Ardö

et al. 2015

Animal

View full text Add to dashboard Cite

In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

show abstract

Section: Introduction: Livestock Behaviour and Vision Systemsmentioning

confidence: 99%

“…Producers have a strong interest in maintaining the welfare of their livestock from both economic and ethical perspectives. As often proactive welfare management has a positive effect on a farm's production efficiency and the quality and marketability of the end product (Kashiha et al, 2013).The observation processes currently used to measure livestock behaviour are subjective, as farm workers perform the welfare assessment. The worker's involvement in these tasks are necessary and therefore increase the demand on the labour and costs associated with monitoring animal behaviour.…”

mentioning

confidence: 99%

A brief review of the application of machine vision in livestock behaviour analysis

Tscharke

Banhazi²

2016

JAI

View full text Add to dashboard Cite

It is desirable to increase the frequency between livestock welfare assessments to enhance problem identification and consumer confidence in livestock welfare management. However, animal welfare is difficult to monitor in practice, due to the inefficiencies involved in manually documenting and determining, animal behaviour, social interaction and health condition of large numbers of animals. Furthermore, the effectiveness of a welfare assessment relies on the intuition of the observer which may vary considerably between assessors. Hence, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Findings indicate that further research is required to develop systems that can monitor the behaviour and welfare of animals' more efficiently and effectively in commercially realistic environments. Introduction: Livestock Behaviour and Vision SystemsMeasuring and assessing the behaviour of livestock is important as it can be used to indicate their welfare status (Dutta et al., 2015;Porto et al., 2014). Behaviour is formed from an animal's continuous interaction with its environment (Figure 1). Animal behaviour is a response to an internal stimuli (physiological) such as hunger or an external stimuli such as climate. If the goal of the behaviour is not, or cannot, be achieved the animal may change its behaviour or physiological response (Busse et al., 2015). There is potential for welfare problems to arise when there are inadequate environmental enrichments to support the behavioural needs of livestock.It has been argued that the husbandry methods used in intensive livestock production have resulted in the deprivation of some naturally occurring behaviours (Fraser, 1983). Producers have a strong interest in maintaining the welfare of their livestock from both economic and ethical perspectives. As often proactive welfare management has a positive effect on a farm's production efficiency and the quality and marketability of the end product (Kashiha et al., 2013).The observation processes currently used to measure livestock behaviour are subjective, as farm workers perform the welfare assessment. The worker's involvement in these tasks are necessary and therefore increase the demand on the labour and costs associated with monitoring animal behaviour. These factors have potential to influence the level of attention each animal receives (Oczak et al., 2013;Pereira et al., 2013). Thus, behavioural measurements are open for interpretation and have potential to be overlooked.Given the problems mentioned above, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Specifically in this article, behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Applying machine vision systems to the behaviour recognition taskTo over...

show abstract

Development of an early warning system for a broiler house using computer vision

Cited by 81 publications

References 19 publications

Towards real-time control of chicken activity in a ventilated chamber

Towards real-time control of chicken activity in a ventilated chamber

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique

A brief review of the application of machine vision in livestock behaviour analysis

Contact Info

Product

Resources

About