Automatic identification of marked pigs in a pen using image pattern recognition

Kashiha, Mohammadamin; Bahr, Claudia; Ott, Sanne; Moons, Christel; Niewold, Theodoor; Ödberg, Frank; Berckmans, Daniël

doi:10.1016/j.compag.2013.01.013

Cited by 109 publications

(75 citation statements)

References 29 publications

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“…Camera technology, in particular, has made it possible to monitor every second of animal behaviour and has also proven especially suitable to study group behaviour (Pastorelli et al, 2006). Authors previously showed that this technology can be helpful for tracking and identifying pigs (Kashiha et al, 2013b) for monitoring behaviours. In current study one of these important behaviours, namely locomotion, was quantified.…”

Section: Resultsmentioning

confidence: 98%

“…The existing techniques require marking colour (Spinka et al, 2004) or IDs (Kashiha et al, 2013b) on pigs. In this study, however, an innovative approach using movement calculation of ellipses fitted to pigs' bodies was chosen to investigate the possibilities of automated locomotion detection for fattening pigs using vision technology.…”

Section: Resultsmentioning

confidence: 99%

“…However, current vision systems need pigs to walk in front of the camera one by one (Lind et al, 2005) or to be marked (Kashiha et al, 2013b;Noldus et al, 2001). Because of this, they can only provide locomotion for the animals as a group (Costa et al, 2007).…”

Section: Introductionmentioning

confidence: 98%

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Automatic monitoring of pig locomotion using image analysis

et al. 2014

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a b s t r a c tThe purpose of this study was to investigate the feasibility and validity of an automated image processing method to detect the locomotion of pigs in a group housed environment and under experimental conditions. Topview video images were captured for forty piglets, housed ten per pen. On average, piglets had a weight of 27 kg (SD ¼ 4.4 kg) at the start of experiments and 40 kg (SD ¼ 6.5) at the end. Each pen was monitored by a topview CCD camera. The image analysis protocol to automatically quantify locomotion involved localising pigs through background subtraction and tracking them over a set period of time. To validate the accuracy of detecting pigs "In Locomotion" or "Not In Locomotion", they were compared to offline manually labelled behavioural data ("In Locomotion" versus "Not In Locomotion"). This is the first study to show that the locomotion of pigs in a group can be determined using image analysis with an accuracy of 89.8%. Since locomotion is known to be associated with issues such as lameness, careful monitoring can give an accurate indication of the health and welfare of pigs.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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Automatic monitoring of pig locomotion using image analysis

et al. 2014

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“…Image processing technology is widely used in precision livestock farming and in supply chain management to identify (Kashiha et al, 2013b), track (Kashiha et al, 2014), and monitor behavior and health status of agricultural animals (Yang et al, 2010;Kashiha et al, 2013c). Image processing systems are comprised of a…”

Section: Introductionmentioning

confidence: 99%

Performance of an image analysis processing system for hen tracking in an environmental preference chamber

et al. 2014

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Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates, and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an environmental animal preference chamber to detect hen navigation between 4 compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared with the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed.

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“…Recent works use techniques of computer vision, neural networks, genetic algorithms, and statistical methods for food and drink classification (Kongsro, 2014;Kashiha et al, 2014;Fernán dez-González et al, 2014;Kashiha et al, 2013;Rodríguez-Pulido et al, 2013). Duarte-Mermoud et al (2010) proposed a method based on Quadratic Discriminant Analysis (QDA) for wine recognition, where features were extracted from liquid chromatograms (based on a diode alignment detector) of polyphenolic compounds present in wine samples.…”

Section: Introductionmentioning

confidence: 99%

A feasibility cachaca type recognition using computer vision and pattern recognition

Rodrigues

Soares

Costa

et al. 2016

Computers and Electronics in Agriculture

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a b s t r a c tBrazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

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Automatic identification of marked pigs in a pen using image pattern recognition

Cited by 109 publications

References 29 publications

Automatic monitoring of pig locomotion using image analysis

Automatic monitoring of pig locomotion using image analysis

Performance of an image analysis processing system for hen tracking in an environmental preference chamber

A feasibility cachaca type recognition using computer vision and pattern recognition

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