Abstract: Aiming at recognition and selection of time period and anomalous data problems， the present study focused on automatic and effective data preprocess methods to offer reliable sample data for the further analysis and modeling. In the present study， the improved cluster analysis was adopted to classify multidimensional time series to obtain the continuous， complete and homogeneous time period. On the basis of that， the suitable anomaly detection criterion was selected to detect and process the outlier， according to the size of sample data. Through the cluster analysis and anomaly detection process of monitoring data about black bean， tomato， pumpkin and cucumber， the data sample suitable for analysis and modeling was chosen effectively. The data preprocess based on cluster analysis and anomaly detection could effectively avoid errors and disturbances in crop monitoring experiments， and improve the quality and efficiency of data analysis and modeling. The method is valuable in the study and application of crop model， in particular crop model for small sample size.

Key words: crop model, monitor experiment, data preprocess, cluster analysis, anomaly detection