Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Abstract

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.

Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification

“…In previous state-of-the-art solutions, such as [ 9 , 10 , 11 , 12 , 13 , 14 ], authors evaluated the classifier in a local computer without providing on-device or cloud-based real-time animal behavior classification. This fact restricts their implementation in a real scenario, since predicting behavioral patterns of the animals after removing the monitoring device from them and processing the stored information is not the optimal use case.…”

“…This kind of network normally uses two types of wireless connections: one for short range, such as Bluetooth or ZigBee, and another one for large range or Internet connection, such as WiFi, GPRS, or LoRA [ 8 ]. Recent studies [ 9 , 10 , 11 , 12 , 13 , 14 ] have proposed collar devices consisting of a GPS module and an IMU (or an accelerometer only) for animal behavior recognition. In [ 9 ], the authors present a device which collects information from a 3-axis accelerometer and a GPS.…”

“…A set of binary time series classifiers were trained and validated offline, achieving an 85% accuracy. In [ 11 ], they used 3-axis neck-mounter accelerometers for dairy cattle behavior classification using a decision tree. Three different behaviors were considered (eating, rumination, and lying), for which the authors reported 99.2% accuracy after training and validating the classifier in Matlab.…”

Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure

“…Using temperature [25] and other biosensors [26] can help to detect a fever state and to assert animal well-being. The most common are inertial sensors based on accelerometers and gyroscopes that are able to identify animal activities such as resting, walking, mating or feeding among others [27,28].…”

Automated Step Detection in Inertial Measurement Unit Data From Turkeys

“…However, adding individual animal identification to the image data is a challenge. Accelerometers are widely applied to individual cows and pigs for detecting behavioral changes over time that can indicate signs of estrus and health or welfare impairment, including lameness (e.g., Pastell et al, 2009;Escalante et al, 2013;Tamura et al, 2019). Inertial measurement units (IMUs) are a combination of accelerometer, gyroscope, and sometimes a magnetometer.…”