Automatically detecting pig position and posture by 2D camera imaging and deep learning

Riekert, Martin; Klein, Achim; Adrion, Felix; Hoffmann, Christa; Gallmann, Eva

doi:10.1016/j.compag.2020.105391

Cited by 93 publications

(74 citation statements)

References 40 publications

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“…Recent advancements focus on monitoring behaviour where the task of detecting problems correspond to users of the system 4 . Developments towards systems that automatically alert staff to behavioural changes are limited to limited behaviours, e.g., lying (or not) 41 , which maybe arbitrary 21 , 23 , 37 . Changes in relevant behaviours are impractical to quantify manually and early detection, through automation, allows for timely intervention to prevent a further reduction in animal welfare and associated economic losses.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…Respiration frequency (P), lying posture and location (G, P), cold/heat vocalisation (G, P), pen/room temperature (G), rectal temperature (P, S), pen fouling prediction (G) [12,13,[15][16][17][18][19][22][23][24][25][26][27][28][29][30]50,[58][59][60]83,84,86,111,112] Ease of movement 17 Space allowance, farrowing crates (presence and size)…”

Section: Good Housing 42mentioning

confidence: 99%

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

2021

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The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time.

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“…Possibly, pig farmers generally consider the recording of animal behaviour to be rather ineffective in improving animal welfare and fear a high additional workload. Although some commercial solutions for automated recording of pig behaviour are already available, there is still a great need for research and development in this regard and for convincing farmers of the benefits of these technologies [ 66 , 67 ]. Cattle farmers might be more relaxed about recording animal behaviour, as systems for automatic recording (e.g., activity, rumination) are already widely used in dairy farming (e.g., [ 68 , 69 ]).…”

Section: Discussionmentioning

confidence: 99%

Farmers’ Preferences Regarding the Design of Animal Welfare Programs: Insights from a Choice-Based Conjoint Study in Germany

Schröter

Mergenthaler

2021

Animals

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As more animal welfare is required in livestock farming, several approaches have been developed to improve the well-being of farmed animals on a voluntary basis. Since farmers’ acceptance is important for the success of these approaches, their preferences should be considered when developing farm animal welfare programs. We used choice based conjoint analysis to investigate the preferences of 242 German livestock farmers (147 cattle farmers; 95 pig farmers) regarding the design of farm animal welfare programs. The conditional logit regression models show that the measures serving as basis for remuneration and the compensation level were of decisive importance for the farmers’ choices. The most preferred measure for assessing animal welfare, and thus as the basis for remuneration, was animal health. As expected, a higher compensation level led to greater acceptance of an animal welfare approach. The commitment period was only of subordinate importance with the longer commitment period being preferred. Our study outlines aspects of farm animal welfare programs that might encourage farmers to participate in these programs. Future programs could consider our findings by emphasising health parameters and by creating planning security through longer commitment periods and sufficiently high compensations for farmers’ efforts to improve animal welfare.

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Automatically detecting pig position and posture by 2D camera imaging and deep learning

Cited by 93 publications

References 40 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

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

Farmers’ Preferences Regarding the Design of Animal Welfare Programs: Insights from a Choice-Based Conjoint Study in Germany

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