Abstract
The cowshed environment has significant impacts on the yield, diseases and behaviors of dairy cows. Heat stress, in particular, has a great impact on yield. The cowshed environment monitoring system based on wireless sensor network can accurately sense the temperature and other environmental parameters in real time and provide basis for manual environmental intervention and control. Energy constraint is one of the important problems that affect the long-term stable monitoring by the dairy cow wireless sensor network. So, the weighted Markov chain method is used to predict the time series of cowshed temperature. Replacing the actual values with the predicted values at the cluster head can effectively reduce data traffic in the cluster, thereby reducing network power consumption. Test data show that, the average variance of the cowshed environment temperature predicted by the method proposed in this paper is 0.185, and the average power consumption is reduced by about 40% when the compression ratio is 0.3, which effectively prolongs the network lifetime. In addition to that, the cowshed environment prediction can also help make pre-judgments for environmental control, reduce or avoid the heat stress of dairy cows after the environmental parameters exceed the thresholds and provide the basis for the multi-source data fusion for dairy cow.
Highlights
Since 2010, dairy farming in China has shifted from family free-ranging to intensive breeding
The LEACH algorithm, which first proposed the concept of clustering, suggested carrying out data fusion at the cluster head to reduce the amount of remote data transmission
Gao Hongju et al proposed a cluster head data fusion method based on the K-means method in 2015, which reduces the amount of data transmission by clustering and preserves data differences while eliminating redundancy [16]
Summary
Since 2010, dairy farming in China has shifted from family free-ranging to intensive breeding. Considering the poor mobility, complex operations and high-power consumption of the current remote monitoring system for cowshed environment, Li Minzan et al from China Agricultural University developed a remote monitoring system based on Ethernet and WeChat platform, which achieves stable and reliable monitoring on the environmental information of dairy farm. Currently there is little research on WSN transmission fusion in dairy farming environment, and the existing WSN data fusion methods mainly focus on data fusion at the cluster head. This paper proposes a WSN spatio-temporal correlation data fusion for dairy cow (STCDF-DC) from the perspective of cowshed temperature time series prediction, which provides basis for the long-term stable operation of dairy cow WSN, early warning against abnormal farming environment and subsequent multi-source data fusion research
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