Abstract
In order to process the massive distributed data, control the agricultural facilities intelligently and improve the production efficiency, a parallel Dirichlet Process Mixture Model (DPMM) clustering method is proposed in this paper based on Spark, which is a memory computing framework. Firstly, the prediction model of skylight opening degree in greenhouse is obtained by training the agricultural environmental and facilities data. Secondly, the model is used to predict the greenhouse skylight opening degree. Thirdly, by compared experiments, both the feasibility and the efficiency of the proposed parallel clustering are verified, the prediction accuracy is also calculated. The experimental results show that the proposed approach has higher efficiency and accuracy.
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Acknowledgments
This work is supported by the Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14DZ1206302. The authors would like to thank editors and anonymous reviewers for their valuable comments and suggestions to improve this paper.
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Yu, Y., Deng, L., Wang, L., Pang, H. (2017). A Skylight Opening Prediction Method Based on Parallel Dirichlet Process Mixture Model Clustering. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_25
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DOI: https://doi.org/10.1007/978-981-10-6373-2_25
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