A software tool for the automatic and real-time analysis of cow velocity data in free-stall barns: The case study of oestrus detection from Ultra-Wide-Band data

Arcidiacono, Claudia; Porto, Simona M.C.; Mancino, Massimo; Cascone, Giovanni

doi:10.1016/j.biosystemseng.2017.10.007

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…Although validated for use on farms (e.g., Tullo et al, 2016), very few studies have examined at the application of these systems in dairy management or combined RTLS location data with activity data recorded from accelerometers. Arcidiacono et al (2017) reported the potential for RTLS to detect estrus in dairy cows and suggested that other applications might include monitoring disease or verifying the welfare status of cows.…”

Section: Introductionmentioning

confidence: 99%

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Barker

Diosdado

Codling

et al. 2018

Journal of Dairy Science

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Section: Introductionmentioning

confidence: 99%

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Barker

Diosdado

Codling

et al. 2018

Journal of Dairy Science

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“…Moreover, the results make clear that supporting farmers with monitoring instrumentation is effectively useful because it allows us to analyze the variables that affect the livestock farming system and animal health and welfare. Therefore, PLF is a valid system to support farmers with decisions that regard animals and the barn management, which has positive effects on the productivity, health and efficiency of the dairy cattle [32][33][34].…”

Section: Discussionmentioning

confidence: 99%

“…The use of adoptable technology following PLF principles is indeed one of the most investigated aspects for current and near future livestock farming. PLF is a solution that involves an initial economic investment but that has undeniable positive effects on the herd management and on guaranteeing environmental, economic and social sustainable livestock productions [24,32,[37][38][39]. Hence, its spread is increasing as it responds to the main requirements of new generations of farmers, consumers and policy makers that look for compromises for having efficient animal productions with satisfactory animal welfare, reduced environmental impact [40][41][42], low inputs purchase/expenses and low wear of resources [5,43].…”

Section: Discussionmentioning

confidence: 99%

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Lovarelli

Mattachini

et al. 2020

Animals

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Due to its increasing pressure on dairy cows, studies that investigate how to cope with heat stress are needed. The heat stress affects multiple aspects of cows’ lives, among which their behavior and welfare. In this study, a survey was carried out in eight farms located in Northern Italy to monitor and evaluate the environmental aspects of the barns and the behavioral responses of dairy cows. For one year, three periods were monitored: thermoneutral (T_S), hot (H_S) and cold (C_S) seasons. Temperature and relative humidity were measured by environmental sensors, and lying vs. standing time, number of lying bouts and their average duration were collected by accelerometers. The temperature-humidity index (THI) was quantified inside and outside of the barn. Results show that at the increase of the THI, behavioral adaptations occurred in all the farms, especially with a reduction of lying time and an increase of respiration rate. Four of the eight farms need interventions for improving the cows’ welfare. Here, environmental problems should be solved by introducing or improving the efficacy of the forced ventilation or by modifying the barn structure. Monitoring dairy barns with sensors and Precision Livestock Farming techniques can be helpful for future livestock farming to alert farmers on the need for their interventions to respond immediately to unwanted barn living conditions.

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“…The position of Anchor 1 was defined as the origin of the plane coordinate system in the barn. behavior recognition, but can also provide a basis to analyze the spatial variability of behaviors with centimeter accuracy for estrus detection [17]. The current study tested the hypothesis that activity, location and machine learning technologies can provide higher accuracy for the detection of estrus in dairy cows than is currently available.…”

Section: Data Acquisition Systemmentioning

confidence: 98%

“…The ultra-wideband (UWB) radio technique has been proven to be accurate in harsh environments, such as those subject to high multipath error and many obstructions, with the possibility for monitoring specific interactions and movements that occur at estrus [16]. Continuous location information achieved by UWB technology can not only advance the accuracy of behavior recognition, but can also provide a basis to analyze the spatial variability of behaviors with centimeter accuracy for estrus detection [17]. The current study tested the hypothesis that activity, location and machine learning technologies can provide higher accuracy for the detection of estrus in dairy cows than is currently available.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

Wang

Bell

Liu

et al. 2020

Animals

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The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection.

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A software tool for the automatic and real-time analysis of cow velocity data in free-stall barns: The case study of oestrus detection from Ultra-Wide-Band data

Cited by 17 publications

References 22 publications

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

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