How many pigs within a group need to be sick to lead to a diagnostic change in the group’s behavior?1

Miller, Amy; Dalton, Hillary A.; Kanellos, Theo; Kyriazakis, Ι.

doi:10.1093/jas/skz083

Cited by 18 publications

(12 citation statements)

References 32 publications

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“…We used a bespoke low-cost data acquisition and processing hardware/software solution that was designed to capture a variety of data (not only video footage). The floor area of the pen was captured with RBG cameras (Microsoft Kinect for Xbox One, Microsoft, Redmond, Washington, USA) attached to the ceiling within ingress protected enclosures and positioned perpendicularly to the pen floor 43 . The camera field of view covered a large space of the pen including the drinking sources, but not the feeding troughs.…”

Section: Methodsmentioning

confidence: 99%

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

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Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of , under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.

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Section: Methodsmentioning

confidence: 99%

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

Self Cite

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“…One of the advantages of the method is that we are able to extract several behaviours from the same pig in real-time. This is beneficial, as it is suggested that a combination of behavioural metrics might be a better indicators of pig health than a change in a single behaviour [40].…”

Section: Discussionmentioning

confidence: 99%

Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning

2019

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Individual pig tracking is key to stepping away from group-level treatment and towards individual pig care. By doing so we can monitor individual pig behaviour changes over time and use these as indicators of health and well-being, which, in turn, will assist in the early detection of disease allowing for earlier and more effective intervention. However, it is a much more computationally challenging than performing this task at group level; mistakes in identification and tracking accumulate and, over time, provide noise measures. We combine a deep CNN object localisation method, Faster Region-based convolutional neural network (R-CNN), with two potential real-time multi-object tracking methods in order to create a complete system that can autonomously localise and track individual pigs allowing for the extraction of metrics pertaining to individual pig behaviours from RGB cameras. We evaluate two different transfer learning strategies to adapt Faster R-CNN to our pig detection dataset that is more challenging than conventional tracking benchmark datasets. We are able to localise pigs in individual frames with 0.901 mean average precision (mAP), which then allows us to track individual pigs across video footage with 92% Multi-Object Tracking Accuracy (MOTA) and 73.4% Identity F1-Score (IDF1), and re-identify them after occlusions and dropped frames with 0.862 mAP (0.788 Rank 1 cumulative matching characteristic (CMC)). From these tracks we extract individual behavioural metrics for total distance travelled, time spent idle, and average speed with less than 0.015 mean squared error (MSE) for each. Changes in all these behavioural metrics have value in the detection of pig health and wellbeing.

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“…Continuous monitoring of changes in animal behavior that occur during development of clinical signs of disease can have diagnostic value and may allow the development of automatic early warning systems (Matthews et al, 2016;Miller et al, 2019;Alameer et al, 2020). Such changes include modifications in feeding, drinking as well as in posture and activity (Martínez-Avilés et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Diarrhea in Weaned Piglets From Individual Feed, Water and Weighing Data

et al. 2021

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This study analyzed individual water and feed consumption related to weight of weaned piglets and their link to diarrhea. Data were collected from 15 batches of 102 piglets each, using specific automata (connected feeders, connected drinkers, automatic weighing stations, RFID ear tags). Analyses were carried out every week on the 138 healthy animals compared by weight category. The average feed consumption had no significant difference between weight categories (light, medium, heavy pigs) whatever the week and was close to 4% of the live weight. For the average water consumption according to weight, it was close to 10%. There was no significant difference between weight groups, except at the end of the period, where the variability of one heavy pig was so high that its own water consumption caused significant difference when compared with the light group. But these overall stable averages do not highlight the high intra-individual variabilities, which was around 40% for both water and feed data at the beginning of trial. At the end, it was almost 16% for feed consumption and 25% for water. The comparison between healthy and diarrheic piglets showed no statistical difference for average water consumption on the day of the first clinical signs and even 1 and 2 days before. In contrast, the average feed consumption had a very significant difference (P ≤ 0.001) for days 5–7 after the weaning and a significant difference for day 8 (P ≤ 0.05). Differences were also significant for data 24 and 48 h before first clinical signs. This means either that diarrheic piglets decrease their feed consumption the first days after weaning or that it is because they eat less that they become diarrheic. So, the hypothesis was that feed consumption could be an interesting indicator to detect early sick animals. Nevertheless, despite this difference, machine learning methods failed in detecting individually diarrheic animals from water and feed consumption related to weight, because of considerable individual variability. To improve these results, one solution could be to collect other data from new sensors like automatic measurement of body temperature or location of piglets in the pen by image analysis.

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How many pigs within a group need to be sick to lead to a diagnostic change in the group’s behavior?1

Cited by 18 publications

References 32 publications

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning

Early Detection of Diarrhea in Weaned Piglets From Individual Feed, Water and Weighing Data

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