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Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques

SDF Convergence Research Department, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
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Electronics 2021, 10(16), 1958; https://doi.org/10.3390/electronics10161958
Received: 28 June 2021 / Revised: 11 August 2021 / Accepted: 11 August 2021 / Published: 14 August 2021
(This article belongs to the Section Artificial Intelligence)
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. View Full-Text
Keywords: anomaly detection; RNN; smart farming; oneM2M; environmental monitoring anomaly detection; RNN; smart farming; oneM2M; environmental monitoring
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MDPI and ACS Style

Park, H.; Park, D.; Kim, S. Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques. Electronics 2021, 10, 1958. https://doi.org/10.3390/electronics10161958

AMA Style

Park H, Park D, Kim S. Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques. Electronics. 2021; 10(16):1958. https://doi.org/10.3390/electronics10161958

Chicago/Turabian Style

Park, Hyeon, Daeheon Park, and Sehan Kim. 2021. "Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques" Electronics 10, no. 16: 1958. https://doi.org/10.3390/electronics10161958

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