Application of Distributed Clustering in Anomaly Detection of Farm Environment Data

Authors

Deng Li, 1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200072, China;
Honglin Pang, 1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200072, China;
Ling Wang, 1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200072, China;
Minrui Fei, 1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;;2. Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200072, China;

Abstract

Abstract: The massive farm environment data stored in the distributed system should be dealt with so as to provide abnormal environment reference and make preventive strategies for crop yield. Considering the characteristics of the farm environment data, the Dirichlet Process Mixture Model (DPMM) clustering is implemented with the farm environment data on Hadoop and the anomaly detection method of the farm environment is proposed based on clustering analysis. Under the framework of MapReduce, Map stage implements the distribution of the sample points to the models; Reduce stage completes the update of models and the number of clusters. The performance has been verified by experiments. The results of clustering and the index of suitable environment for tomato are compared to implement the anomaly detection. The analysis results show that the method can be applied to anomaly detection of large number of farm environment data.

Recommended Citation

Li, Deng; Pang, Honglin; Wang, Ling; and Fei, Minrui (2020) "Application of Distributed Clustering in Anomaly Detection of Farm Environment Data," Journal of System Simulation: Vol. 29: Iss. 12, Article 14.
DOI: 10.16182/j.issn1004731x.joss.201712014
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss12/14

First Page

3035

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201712014

Last Page

3041

CLC

TP338.8

Recommended Citation

Deng Li, Pang Honglin, Ling Wang, Minrui Fei. Application of Distributed Clustering in Anomaly Detection of Farm Environment Data[J]. Journal of System Simulation, 2017, 29(12): 3035-3041.

DOI

10.16182/j.issn1004731x.joss.201712014