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

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.

Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

“…The optimal training parameter setup values are shown in Table 5 [147]. Park et al [148] proposed a machine-to-machine (M2M) standard communication method between things within the IoT environment in order to address the fact that most IoT services and devices are implemented to operate in a prototyped zone limited to the location where the experiment is taking place. The proposed method paved the way toward more interactive and well-connected system sensors and controlled devices within smart farm zones inside IoT environments.…”

“…The M2M method offers functions including remote configuration, operation instruction, connections, data collection, data storage, device management, and security. In the same study [148], the compiled and processed information, basically generated from different IoT-based devices within livestock houses as shown in Figure 9. Livestock houses and farm communication structure using the M2M approach [148], was transmitted and received according to the M2M standard method.…”

“…In the same study [148], the compiled and processed information, basically generated from different IoT-based devices within livestock houses as shown in Figure 9. Livestock houses and farm communication structure using the M2M approach [148], was transmitted and received according to the M2M standard method.…”

“…The livestock houses consisted of multiple pig barns, each of which had a set of IoT equipment as shown in Table 6 [148]. Creating a predictive model for each device is essential in order to identify multidevice failure situations in livestock houses.…”

Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture

“…For time series data generated in agricultural IoT systems, some researchers have focused on anomaly detection of sensor network data. Several papers have offered specifics on anomalies in smart ecosystems ( Cook et al, 2019 ; Hasan et al, 2019 ; Park et al, 2021 ). In smart agriculture scenarios, agricultural IoT devices are often exposed to harsh conditions that can lead to failure of the device itself, compromised communications, or malicious attacks, which can lead to data anomalies.…”