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With the wide application of various agriculture IOT sensor equipment, sensor networks produce ample data for observing dynamic soil properties in farmland. But data processing for analysis and visualization become increasingly difficult as data dimensionality increases. Meanwhile the devices sometimes lose data because of damage and power outages. In this study, exploratory data analysis (EDA) and spatiotemporal interpolation including time as a third dimension and spatial-temporal variogram are used to analyze the soil moisture and temperature data. The experimental results show that EDA can effectively reveal the implicit information of soil sensing data in multiple perspectives by a variety of visual ways. The leave-onestation- out cross-validation indicate that Kriging method based on spatial-temporal variogram has satisfied accuracy, which supplies an effective approach for interpolation and estimation of lost agriculture IOT sensing data. The results also have theoretic and practical value for regional soil management.

Research on Online Mining Method of Real Time Soil Sensing Data in Farmland