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

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.

Integrated Data Augmentation for Accelerometer Time Series in Behavior Recognition: Roles of Sampling, Balancing, and Fourier Surrogates

“…On the contrary, natural and artificial processes exhibit some activity patterns more frequently than others because their daily functioning requires it. Similar situations are encountered when considering wearable systems for livestock activity monitoring [10], human behavior classification [11], gesture recognition [12], as well as room occupancy and activity detection [13].…”

Towards a novel method for detecting atypical lying down and standing up behaviors in dairy cows using accelerometers and machine learning

“…Crawling backwards (yes/no) Backwards movement on carpal joints after the head lunge Yes: 71 (13.0%) [21] No: 477 (87.0%) [48] NA: 21 [16] LHL, RFL, LFL sensitivity and specificity (Eq. 1, [50]).…”

“…Sensors, such as accelerometers, are now frequently used to study and monitor cow behavior. From acceleration data, general activities [17][18][19], lying behavior [20,21], grazing and rumination behavior [22][23][24], and health problems such as lameness can be tracked [25,26]. This data gives valuable insight into the welfare and health of animals, and enables farmers, veterinarians, and researchers to make informed decisions [27].…”

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

“…In recent years, contact sensors (including accelerometers, inertial measurement units (IMU), pedometers, and magnetometers) have usually been designed to collect different behavioral movements and recognize and track animal behaviors [11,19,[34][35][36]. It is reported that wearable behavior-monitoring systems that are integrated with sensors, such as collars, ear tags, and leg bands, have been used to autonomously identify dairy cow behavior while minimizing human interference or human error [12,35,37,38]. Together with the sensors, machine-learning techniques were applied, including various algorithms, such as random forest (RF), decision tree (DT), and K-nearestneighbors (KNN), to classify the different behaviors of cows [36,[38][39][40].…”