Abstract: In order to process the massive agricultural environmental data stored in distributed system and improve the production efciency, the clustering algorithm based on Gaussian Mixture Model (GMM) is modifed in this paper. Based on this, an environmental anomaly detection method during crop growth is proposed. Under the Spark distributed computing framework, frstly, a pre-clustering algorithm is adopted to initialize the models. Secondly, Spark is utilized to update the models iterationally until it gets stable. In each iteration, Map phase distributes sample points to the models, Reduce phase renews the numbers of models and parameters. Finally, the detection of environmental anomaly is completed by taking advantages of the clustering result. The experimental results show that this approach is practically feasible and effective.

Key words: agriculture environmental data, anomaly detection, Spark, Gaussian mixture model (GMM) clustering