Experimental characterisation of the off-body wireless channel at 2.4 GHz for dairy cows in barns and pastures

status: publishe

Optimal energy storage solution for an inductively powered system for dairy cows

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Wireless energy transfer by means of inductive coupling for dairy cow health monitoring

The increase of herd sizes hinders the capability of the dairy farmer to timely detect illnesses. Therefore, automatic health monitoring systems are deployed, but due to their high energy consumption, the application possibilities remain limited. In this work, a wireless, inductive charging solution for dairy cow monitoring is designed. This system is mounted at the eating trough, and the amount of energy transferred each eating turn is determined experimentally. For the first time, inductive wireless power transfer is used to charge on-body sensor networks for cattle. Measurements at a research farm on 40 dairy cows show an average energy transfer of 96 J per meal, for an average eating time of 160 s. It is demonstrated that inductive power transfer is a viable technology to resolve the energy provision challenge for the automatic and real-time health monitoring of dairy cows

On the use of on-cow accelerometers for the classification of behaviours in dairy barns

Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6 h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1 Hz to 0.05 Hz

Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers

A new simple decision-tree (DT) algorithm was developed using the data from a neck-mounted accelerometer for real-time classification of feeding and ruminating behaviours of dairy cows. The performance of the DT was compared to that of a support vector machine (SVM) algorithm and a RumiWatch noseband sensor and the effect of decreasing the sampling rate of the accelerometer on the classification accuracy of the developed algorithms was investigated. Ten multiparous dairy cows were used in this study. Each cow was fitted with a RumiWatch halter and an accelerometer attached to the cow's collar with both sensors programmed to log data at 10 Hz. Direct observations of the cows' behaviours were used as reference (baseline data). Results indicate that the two sensors have similar classification performances for the considered behavioural categories (i.e., feeding, ruminating, other activity), with an overall accuracy of 93% for the accelerometer with SVM, 90% for the accelerometer with DT, and 91% for the Rumiwatch sensor. The difference between the predicted and the observed ruminating time (in min/h) was less than 1 min. h (1.5% of the observed time) for the SVM and less than 2 min. h (2.8%) for both DT and the RumiWatch. Similarly, the difference in feeding time was 1.3 min. h (2.1%) for the SVM compared to 2.5 min. h (4.3%) and 2.4 min. h (4.1%) for both RumiWatch and DT, respectively. These preliminary findings illustrate the potential of the collar-mounted accelerometer to classify feeding and ruminating behaviours with accuracy measures comparable to the Rumiwatch noseband sensor