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Journal Article Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques
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Authors
Hyeon Park, Daeheon Park, Sehan Kim
Issue Date
2021-08
Citation
Electronics, v.10, no.16, pp.1-22
ISSN
2079-9292
Publisher
MDPI
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/electronics10161958
Abstract
In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.
KSP Keywords
Environmental Factors, Environmental sensors, Growth conditions, Livestock farms, Pig house, Predictive model, Real-time, Smart farm, analysis of data, anomaly detection, deep learning(DL)
This work is distributed under the term of Creative Commons License (CCL)
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