Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers

Benaissa, Said; Tuyttens, Frank; David, Paul A.; Cattrysse, Hannes; Martens, Luc; Vandaele, Leen; Joseph, Wout; Sonck, Bart

doi:10.1016/j.applanim.2018.12.003

Cited by 79 publications

(38 citation statements)

References 40 publications

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“…Studies on cattle behavior have indicated that illness causes animals to spend less time feeding and more time resting [ 43 ]. Feeding and ruminating behavior in cattle provides significant information about their productivity, health, and welfare [ 44 ]. Changes in ruminating and feeding timing of cows are indicative of an underlying alteration in their welfare and comfort [ 45 ].…”

Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

“…Wearable accelerometers have been evaluated as a tool for measuring cow behavior [ 44 ]. HOBO accelerometers, which are attached to the jaws of cows, can record the time each animal spends grazing and ruminating [ 47 ].…”

Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

“…Monitoring this behavior on a large scale becomes impractical because of the manpower required for continuous monitoring of a large groups of animals. Wearable sensor technologies make it possible to simultaneously measure real-time physiological parameters in a herd on a large scale when coordinated with appropriate interpretations of outputs [ 44 ]. Thus, wearable sensor technologies have an edge over traditional herd-based approaches as the data from wearable sensors can be analyzed immediately, enabling quick reaction times.…”

Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

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Transforming the Adaptation Physiology of Farm Animals through Sensors

Neethirajan¹

2020

Animals

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Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, color, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.

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Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

Section: Assessing Adaptation Physiology Parametersmentioning

confidence: 99%

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Transforming the Adaptation Physiology of Farm Animals through Sensors

Neethirajan¹

2020

Animals

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“…Vá zquez-Diosdado [6] designed a mixed multi-level model to automatically detect the drinking behavior of dairy cows, with environmental temperature, parity, and average monthly milk production as variables. Benaissa [7] installed accelerometers on cows to identify their feeding, rumination, and other digestive activities and classifying the behaviors with a DT model. By installing an inertial measurement unit on the backs of cows, Achour [8] monitored the standing, lying on the side, and walking behaviors of cows.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

et al. 2021

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The feeding, ruminating(standing, lying down), running, being still (standing, lying down), head-shaking, drinking, and walking behaviors of dairy cows can reflect their health status. In this study, a multi-sensor was used to collect data of cow's multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system was designed using geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments among different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.

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“…In these studies, the main focus was put on gait parameters-step overlap, hoof release angle and back arch curvature [5][6][7]. Raw data of Benaissa et al (2019) [8]. illustrate that the widely used collar-mounted accelerometer classifies ruminating and feeding behaviors with accuracy closely comparable to the RumiWatch noseband sensor [8].…”

Section: Introductionmentioning

confidence: 99%

Impact of Lameness on Attributes of Feeding Registered with Noseband Sensor in Fresh Dairy Cows

et al. 2021

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We hypothesized that lameness in fresh dairy cows (1–30 days after calving) has an impact on attributes of feeding registered with a noseband sensor. The aim of this study was to investigate the impact of lameness in fresh dairy cows on attributes of feeding (registered with the RumiWatch noseband sensor): rumination time (RT), drinking time (DT), eating time (ET), rumination chews (RC), eating chews (EC), chews per minute (CM), drinking gulps (DG), bolus count (B), and chews per bolus (CB). The measurement registration was started at the first day after calving and continued until 30 days after calving. There were 20 Lithuanian black and white breed cows selected. Lameness diagnosis was performed by trained staff based on a locomotion score system and it was diagnosed on average on the 15th day after calving. The causes of lameness were categorized as sole ulcer, abscess and foot rot. Special attention was paid to attributes of feeding registered 14 days before and 13 days after diagnosis. The 10 lame cows (LG) used in this experiment had a lameness score of 3–4 presented with severe lameness: they were reluctant to move and unwilling to complete weight transfer off the affected limb. The 10 healthy cows (HG) were given a lameness score of 1. We found that lameness of fresh dairy cows has an impact on inline registered ingestive behaviors biomarkers—the mean RT of HG cows was as much as 2.19 times higher than that of LG cows on the day of diagnosis of lameness, later this difference between the groups decreased to the sixth day of treatment, then increased again and decreased at the end of the experiment. The lowest eating time was found on diagnosis day and the highest on the ninth day before determination of lameness. Drinking time was higher in the HG group, with the exception of 10 and 9 days prior to clinical signs of disease in LG cows. A downward trend in rumination chews was observed in LG cows from day 7 until the onset of clinical symptoms. The bolus count decreased from day 3 before diagnosis to day 1 after diagnosis in LG cows. The largest difference in this indicator between groups was found on day of diagnosis. Analysing the pattern of CM values in the LG group, we found a decrease from 10 days before to 2 days after diagnosis. The CB value was almost the same in both groups of cows at the end of the experiment, but largest difference between the groups was found on day 7 after clinical sings of lameness.

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Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers

Cited by 79 publications

References 40 publications

Transforming the Adaptation Physiology of Farm Animals through Sensors

Transforming the Adaptation Physiology of Farm Animals through Sensors

Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information

Impact of Lameness on Attributes of Feeding Registered with Noseband Sensor in Fresh Dairy Cows

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