Abstract
Large-scale agricultural internet of things will generate a large amount of data every moment. After a certain period of time, the amount of data can reach hundreds of millions. It is very meaningful to analyze and mine agricultural big data and replace artificial experience with analysis results. However, the agricultural production environment is complex, and the raw data collected include a variety of anomalies, which can not be directly followed by analysis and mining. In this paper, a data preprocessing method based on time series analysis is proposed, which can quickly and efficiently obtain the prediction model, and can be used to fill and replace the abnormal data. On this basis, we add data preprocessing layer to the traditional three-layer Internet of things system (IoT), which is located between the application layer and the transmission layer, and designs a four layer of Agricultural IoT system. The system not only realizes the basic functions of data acquisition, transmission and storage, but also provides better data sources for subsequent analysis.
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Ma, Y., Jin, J., Huang, Q., Dan, F. (2018). Data Preprocessing of Agricultural IoT Based on Time Series Analysis. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_21
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DOI: https://doi.org/10.1007/978-3-319-95930-6_21
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