High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Abstract

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

“…However, these approaches exhibit two primary shortcomings. Firstly, they tend to possess inherent limitations in terms of scope [ 41 , 42 , 43 ]. Secondly, they might overlook latent nonlinear and sophisticated features present within the data.…”

Video Validation of Tri-Axial Accelerometer for Monitoring Zoo-Housed Tamandua tetradactyla Activity Patterns in Response to Changes in Husbandry Conditions

“…Other studies have shown that accelerometers are useful for clearly discriminating between resting behaviour and active behaviours. However, the reduced performance of accelerometers is evident based on accuracy and precision parameters when they were applied to monitor behaviours with low acceleration, such as feeding and other movements [ 53 , 57 , 58 ]. Nevertheless, when researchers want to analyse results, it is also important to consider the position of the accelerometer on the animal body (e.g., ear or neck) and/or the species-specific feeding strategy; for example, results on feeding behaviour collected using accelerometers mounted on the necks of cattle will be different for grazing animals in a field vs. animals in a feedlot [ 38 , 59 ].…”

Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems

“…Multilayer Perceptions (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM) [59,60] 2.5.…”