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      Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn

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      Animals
      MDPI AG

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          Abstract

          Monitoring systems assist farmers in monitoring the health of dairy cows by predicting behavioral patterns (e.g., lying) and their changes with machine learning models. However, the available systems were developed either for indoors or for pasture and fail to predict the behavior in other locations. Therefore, the goal of our study was to train and evaluate a model for the prediction of lying on a pasture and in the barn. On three farms, 7–11 dairy cows each were equipped with the prototype of the monitoring system containing an accelerometer, a magnetometer and a gyroscope. Video observations on the pasture and in the barn provided ground truth data. We used 34.5 h of datasets from pasture for training and 480.5 h from both locations for evaluating. In comparison, random forest, an orientation-independent feature set with 5 s windows without overlap, achieved the highest accuracy. Sensitivity, specificity and accuracy were 95.6%, 80.5% and 87.4%, respectively. Accuracy on the pasture (93.2%) exceeded accuracy in the barn (81.4%). Ruminating while standing was the most confused with lying. Out of individual lying bouts, 95.6 and 93.4% were identified on the pasture and in the barn, respectively. Adding a model for standing up events and lying down events could improve the prediction of lying in the barn.

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          • Record: found
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          Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

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            • Record: found
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            • Article: not found

            The control of the false discovery rate in multiple testing under dependency

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              • Record: found
              • Abstract: not found
              • Article: not found

              The Internet of Things: A survey

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                Journal
                Animals
                Animals
                MDPI AG
                2076-2615
                September 2021
                September 10 2021
                : 11
                : 9
                : 2660
                Article
                10.3390/ani11092660
                af0e20b4-75f4-4cb1-8105-5d1f93409943
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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