Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows’ Behavior

Abstract

Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: 1) acquisition mode, where a mobile application supported the raw data collection during observations; and 2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.

Application of Precision Technologies to Characterize Animal Behavior: A Review

“…The combination of accelerometers and GPS results in a synergistic relationship that exploits the strengths of both sensors to provide a good understanding of ruminants. Australia Accuracy of 88% to 98% in monitoring licking behavior [42] Australia 4-month-old calves suckled fewer times, but for longer [73] United Kingdom Classification of rumination, eating, and other behaviors with precision of 0.83 [74] Pasture-based France The accuracy of prediction of the main behaviors was 98% [40] Semi-enclosed barn United States Accuracy of rumination detection was 86.2% [41] Three dairy farms Italy Accuracy of behavior detection was 85.12% [75] Dairy farm Italy Accuracy of classifying behavior was 96% [76] GPS Extensive United States Cattle followed water more than salt [3] Hungary Weather fronts affected the herd's route [64] Pasture-based Malaysia Observation of the grazing patterns was accurate [63] England Cattle tended to favor shorter material during the day and material of higher crude fiber in the evening [66] Commercial farm Spain Sensor was able to detect hotspots of dung deposition [77] GPS-GPRS Extensive Spain Distance traveled daily was 3147 m [65] Accelerometer, GPS Pasture-based Australia Description of the animals' movement and some behaviors was successful [78] Spain Accuracy of classification of behavior was 93% [70] Accelerometer, RFID Pasture-based Australia Accelerometer correlated highly with the observed duration of drinking events [79] Accelerometer, magnetometer Intensive Tasmania Grazing, ruminating, and resting were identified accurately [80] Accelerometer, cameras Intensive China Accuracy of 94.9% in recognizing behavior [81] Table 1. Cont.…”

A New Dataset for Video-based Cow Behavior Recognition

“…In addition, research on wearable devices is also a hot topic in the field of sensors.In the study conducted by Lovarelli et al, they focused on developing a novel wearable 3D sensor node and an innovative open classification system specifically designed for monitoring dairy cows' behavior [6]. The researchers aimed to create a customizable device that could accurately classify various behaviors exhibited by dairy cows.…”

Systematic Review on Internet of Things in Smart Livestock Management Systems

“…The disadvantage of this method is that the batteries in the cow tag and cow collar frequently need to be replaced. Lovarelli et al [53] designed custom devices to identify cow behaviors such as eating, lying, and chewing. Instead of using commercial sensors, the authors created their own using the EFR32BG13 Blue Gecko SiP.…”