Online lameness detection in dairy cattle using Body Movement Pattern (BMP)

Poursaberi, Ahmad; Bahr, Claudia; Pluk, Arno; Berckmans, Daniël; Veermäe, I.; Kokin, E.; Pokalainen, Vaino

doi:10.1109/isda.2011.6121743

Cited by 30 publications

(26 citation statements)

References 5 publications

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“…An allencompassing definition of lameness includes any abnormality which causes a cow to change the way that she walks, and can be caused by a range of foot and leg conditions, themselves caused by disease, management or environmental factors [3]. Prevention, early detection and treatment of lameness is therefore important to reduce these negative effects of lameness on dairy cows [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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

Byabazaire

Olariu²,

Taneja

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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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.

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Section: Introductionmentioning

confidence: 99%

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

Byabazaire

Olariu²,

Taneja

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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“…The proposed systems use ZigBee based wireless sensor network to detect the body temperature and movement. Another effort [44] focuses on detecting lameness in cows using camera sensors in real time to detect the curve formation by the head position and back posture. Paper [45] deploys a sensing climate control system for indoor cattle farming to improve the comfort level of animals and detect disease.…”

Section: A Indoor Farm Animalsmentioning

confidence: 99%

Smart Computing and Sensing Technologies for Animal Welfare

2017

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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 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 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.

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“…In another work of lameness observation, Poursaberi et al used a vision-based approach based on back posture analysis [5]. Their technique separated the back of the cow from the background, enabling the joint analysis of back posture and head position, called Body Movement Pattern (BMP).…”

Section: Vision-based Approachmentioning

confidence: 99%

“…It is too laborious and costly. Consequently, several researchers have tried to develop an automated system that can recognize cows' behaviour with little human effort such as these studies [2][3][4][5][6].Two main approaches for tackling this problem are sensor-based technique and vision-based technique. Between the two approaches, vision-based technique is more suitable for detecting dairy cows' behaviours because it is less costly compared to the sensor-based approach.…”

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confidence: 99%

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A comparative study of feature point matching versus foreground detection for computer detection of dairy cows in video frames

Pasupa

Pantuwong

Nopparit

2015

Artif Life Robotics

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cows are dictated by their health condition and their physical environment, behaviours such as their frequencies of drinking, eating, lying, and rumination. Regular observation of dairy cows' behaviours ensures their good health and early detection of an abnormal posture or activity can help control the ailment. Dairy cows that are physically and emotionally healthy produce more high-quality milk [1].At present, it needs a human to observe and analyse dairy cows' behaviours. This person has to be well trained and experienced. However, a human cannot manually observe every cow in a farm all the time. It is too laborious and costly. Consequently, several researchers have tried to develop an automated system that can recognize cows' behaviour with little human effort such as these studies [2][3][4][5][6].Two main approaches for tackling this problem are sensor-based technique and vision-based technique. Between the two approaches, vision-based technique is more suitable for detecting dairy cows' behaviours because it is less costly compared to the sensor-based approach. Sensorbased approach requires at least one sensor for every cow and a team of technical staff to operate the system. Moreover, there is a risk of reduced milk production because the cows may be annoyed by the sensor. The ultimate goal of our studies is to create an automated vision-based system for detection and analysis of dairy cows' behaviours.As the first step in this endeavour, we explored the possibility of detecting dairy cows in a free-stall barn by the two following vision-based methods: (i) Feature point matching method: finding point correspondences between the reference and the target images using Speeded Up Robust Features (SURF) [7], a performance scale and a rotation-invariant interest point detector and descriptor.Abstract Behaviours of dairy cows reflect their health and emotions. Behavioural analysis by video surveillance is an accepted technique for helping cow-keepers to spot their cows' health problems. To perform a behavioural analysis, the presence and location of the cows need to be detected first. In this study, we used feature point matching method and foreground detection method to detect them. Two experiments were conducted in a dairy farm to detect cows in video frames recorded by a video camera installed over the top of a free-stall barn. A total of 800 frames of recorded cows' activities were captured. True and false positive and negative results were statistically confirmed by t test. We found that the accuracies of the feature point matching and foreground detection methods were 38.55 and 75.95 %, respectively; hence, for our setup, the foreground detection was a better method.

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Online lameness detection in dairy cattle using Body Movement Pattern (BMP)

Cited by 30 publications

References 5 publications

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

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

Smart Computing and Sensing Technologies for Animal Welfare

A comparative study of feature point matching versus foreground detection for computer detection of dairy cows in video frames

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