A vision guided robot for tracking a live, loosely constrained pig

Frost, A.R.; French, A. P.; Tillett, R.D.; Pridmore, Tony; Welch, Sean

doi:10.1016/j.compag.2004.03.003

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…The primary objective of PLF is to develop livestock management and monitoring systems with technologies to support the farmer (Berckmans, 2014). This includes the use of sensor technology for observing animals (Darr and Epperson, 2009), the application of modern control theory to improve autonomy of the production process (Frost et al, 2004), and the use of advanced data processing methods to synthesise and combine different types of data (Terrasson et al, 2016). Precision livestock farming is based on the interaction between different scientific disciplines and stakeholders in the livestock industry.…”

Section: The Principles Of Precision Livestock Farmingmentioning

confidence: 99%

“…In the process of behavioural feature extraction, the difficulty lies in the location of individual feature points on the animal body including, for example, the location of the animals head and tail (Kashiha et al, 2013a). Without going into further detail, existing methods for locating feature points include the point distribution model (Cangar et al, 2008) and the kink points method (proposed by Frost et al, 2004). By analysing the motion of these feature points between adjacent image frames, more accurate motion features and position features can be extracted.…”

Section: Image Analysismentioning

confidence: 99%

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Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

123

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Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ImplicationsThis work address the main approaches utilised in developing precision livestock farming tools. Precision livestock farming is an approach that enables the farmer with more objective information about the animal to make better choices about the sustainability of their production system. This paper demonstrates some of the key solutions and the approaches taken to develop technologies with sound and image analyses. †

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Section: The Principles Of Precision Livestock Farmingmentioning

confidence: 99%

Section: Image Analysismentioning

confidence: 99%

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

123

View full text Add to dashboard Cite

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“…This problem can be alleviated by constraining the animal of interest so that it is in a standard position with no other animals around. This has been applied to pigs to monitor weight (Schofield et al, 1999) and back fat (Frost et al, 2004). Leroy et al (2006) developed an automatic computer vision technique to track individual laying hen and detect six different behavior phenotypes: standing, sitting, sleeping, grooming, scratching, and pecking.…”

Section: 2 CM Of Perch Per Hen) On Bird Behaviors Corresponding To mentioning

confidence: 99%

Automated Tracking and Behavior Quantification of Laying Hens Using 3D Computer Vision and Radio Frequency Identification Technologies

2014

Trans. ASABE

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Housing design and management schemes (e.g., bird stocking density) in egg production can impact hens' ability to perform natural behaviors and production economic efficiency. It is therefore of socio-economic importance to quantify the effects of such schemes on laying-hen behaviors, which may in turn have implications on the animals' well-being. Video recording and manual video analysis is the most common approach used to track and register laying-hen behaviors. However, such manual video analyses are labor intensive and are prone to human error, and the number of target objects that can be tracked simultaneously is small. In this study, we developed a novel method for automated quantification of certain behaviors of individual laying hens in a group-housed setting (1.2 m × 1.2 m pen), such as locomotion, perching, feeding, drinking, and nesting. Image processing techniques were employed on top-view images captured with a stateof-the-art time-of-flight (ToF) of light based 3D vision camera for identification as well as tracking of individual birds in the group with support from a passive radio-frequency identification (RFID) system. Each hen was tagged with a unique RFID transponder attached to the lower part of her leg. An RFID sensor grid consisting of 20 antennas installed underneath the pen floor was used as a recovery system in situations where the imaging system failed to maintain identities of the birds. Spatial as well as temporal data were used to extract the aforementioned behaviors of each bird. To test the performance of the tracking system, we examined the effects of two stocking densities (2880 vs. 1440 cm2 hen-1) and two perching spaces (24.4 vs. 12.2 cm of perch per hen) on bird behaviors, corresponding to five hens vs. ten hens, respectively, in the 1.2 m × 1.2 m pen. The system was able to discern the impact of the physical environment (space allocation) on behaviors of the birds, with a 95% agreement in tracking the movement trajectories of the hens between the automated measurement and human labeling. This system enables researchers to more effectively assess the impact of housing and/or management factors or health status on bird behaviors.

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“…Monitoring the behavior of a particular animal in a group requires information obtained from tracking the specific animal and this can be achieved by limiting the animal’s activity to ensure that it remains in an appropriate location without other animals in its vicinity. This idea has been applied to monitor a pig’s weight [ 12 ] and back fat levels [ 13 ] and to monitor a laying hen’s activities [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

Wang

Chen

Zhang

et al. 2016

J Animal Sci Biotechnol

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BackgroundBehavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem.ResultsIn this study, an algorithm based on a Hybrid Support Vector Machine (HSVM) was developed for the automated tracking of individual laying hens in a layer group. More than 500 h of video was conducted with laying hens raised under a floor system by using an experimental platform. The experimental results demonstrated that the HSVM tracker outperformed the Frag (fragment-based tracking method), the TLD (Tracking-Learning-Detection), the PLS (object tracking via partial least squares analysis), the MeanShift Algorithm, and the Particle Filter Algorithm based on their overlap rate and the average overlap rate.ConclusionsThe experimental results indicate that the HSVM tracker achieved better robustness and state-of-the-art performance in its ability to track individual laying hens than the other algorithms tested. It has potential for use in monitoring animal behavior under practical rearing conditions.

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A vision guided robot for tracking a live, loosely constrained pig

Cited by 13 publications

References 6 publications

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Automated Tracking and Behavior Quantification of Laying Hens Using 3D Computer Vision and Radio Frequency Identification Technologies

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

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