Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Dynamic time warping for classifying lameness in cows

Lameness in dairy cows is one of the most significant welfare and productivity issue. This work is mainly concerned with an automated detecting system for classifying lameness in dairy cows. In the proposed system, Dynamic Time Warping (DTW) is used to measure the similarity between two-time series. The first time series is the behavioral time periods of the cow used as the templates, which was collected while the cow was sound. The second time series is the behavioral time periods of the cow on each day used for testing. This process results in accumulated distance that is compared with a threshold value for classifying lameness. In the case of studies, three cows were used in experiments. The classified results show that the proposed algorithm can correctly classify lame and non-lame cows

Early and non-intrusive lameness detection in dairy cows using 3-dimensional video

ABSTRACTLameness is a major issue in dairy herds and its early and automated detection offers animal welfare benefits together with high potential commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness. A novel proxy for lameness using 3-dimensional (3D) depth video data to analyse the animal’s gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint during walking. The video capture setup is completely covert and it facilitates an automated process. The animals are recorded using an overhead 3D depth camera as they walk freely in single file after the milking session. A 3D depth image of the cow’s body is used to automatically track key regions such as the hooks and the spine. The height movements are calculated from these regions to form the locomotion signals of this study, which are analysed using a Hilbert transform. Our results using a 1-5 locomotion scoring (LS) system on 22 Holstein Friesian dairy cows, a threshold could be identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome thereby minimising losses and reducing animal suffering. Using a linear Support Vector Machine (SVM) binary classification model, the threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows) and 75% specificity (detecting non-lame cows)

Video tracking of dairy cows for assessing mobility scores

Lameness afflicts a large proportion of dairy herds, but could be considerably reduced by automated monitoring by CCTV. Key to this is reliable, robust detection and tracking of individual cows in crowded video sequences. We introduce a novel detection and tracking method, based on the Viola-Jones detector. We show that animals can be tracked and their overall gait patterns and speed automatically extracted from video sequences. Preliminary work on identification of individual animals through principal component analysis and SIFT feature matching is also described

Objektiv rörelseanalys för mätning av hälta hos kliniskt halta kor

Hälta hos mjölkkor medför lidande för korna och ekonomiska förluster för djurhållaren. En automatiserad metod för hältdetektion som går att använda i stallmiljö skulle kunna leda till tidigare upptäckt av hälta och minska de negativa konsekvenserna. Ett i praktiken välfungerande system för automatiserad hältövervakning finns inte idag och för utveckling av en sådan metod behöver rörelsemönstret hos halta och friska kor förstås bättre. Syftet med denna studie var att undersöka om huvudets och sacrums vertikala rörelse i skritt kan användas för att objektivt avgöra om en ko är halt eller inte. Därför mättes kliniskt halta kor med accelerometerbaserade systemet EquiMoves före och efter behandling av hälta och det vertikala rörelsemönstret analyserades. Slutsatsen dras att systemet tillåter upptäckt av bakbenshälta genom att mäta sacrums rörelse, även om ett fåtal kor omfattas av mätningarna. Ytterligare studier behövs för att utvärdera tillämparheten samt vidare analys av rörelsevariabler för frambenhälta.Lameness in dairy cows is related to pain for the cows and economical losses for the farmer. An automated method for lameness detection that could be used in a stable could lead to earlier detection of disease, and reduce the negative consequences of the same. Such a method does not exist today, and to develop such a method the motion pattern for lame and healthy cows needs to be understood. The aim of this study was to investigate if the vertical displacement of the head and sacrum during walk can be used to objectively discern whether or not a cow is lame. Therefore, clinically lame cows were measured with the accelerometer-based system EquiMoves before and after treatment of lameness and the vertical motion was analyzed. To conclude, the system allows hindlimb lameness to be detected, even if only a few cows are measured. Additional studies are required to evaluate the applicability for front leg lameness

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Initial validation of an intelligent video surveillance system for automatic detection of dairy cattle lameness

IntroductionLameness is a major welfare challenge facing the dairy industry worldwide. Monitoring herd lameness prevalence, and early detection and therapeutic intervention are important aspects of lameness control in dairy herds. The objective of this study was to evaluate the performance of a commercially available video surveillance system for automatic detection of dairy cattle lameness (CattleEye Ltd).MethodsThis was achieved by first measuring mobility score agreement between CattleEye and two veterinarians (Assessor 1 and Assessor 2), and second, by investigating the ability of the CattleEye system to detect cows with potentially painful foot lesions. We analysed 6,040 mobility scores collected from three dairy farms. Inter-rate agreement was estimated by calculating percentage agreement (PA), Cohen’s kappa (κ) and Gwet’s agreement coefficient (AC). Data regarding the presence of foot lesions were also available for a subset of this dataset. The ability of the system to predict the presence of potentially painful foot lesions was tested against that of Assessor 1 by calculating measures of accuracy, using lesion records during the foot trimming sessions as reference.ResultsIn general, inter-rater agreement between CattleEye and either human assessor was strong and similar to that between the human assessors, with PA and AC being consistently above 80% and 0.80, respectively. Kappa agreement between CattleEye and the human scorers was in line with previous studies (investigating agreement between human assessors) and within the fair to moderate agreement range. The system was more sensitive than Assessor 1 in identifying cows with potentially painful lesions, with 0.52 sensitivity and 0.81 specificity compared to the Assessor’s 0.29 and 0.89 respectively.DiscussionThis pilot study showed that the CattleEye system achieved scores comparable to that of two experienced veterinarians and was more sensitive than a trained veterinarian in detecting painful foot lesions

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g. step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer’s mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Index Terms—Lameness, Internet of Things (IoT), Data Analytics, Smart Agriculture, Machine Learning, Micro services, Fog Computing. I