Research On Crop Model For Small Sample Size

Crop model is one of the important means of precision agriculture,digital agriculture and intelligent agriculture.In recent years,the research of the crop growth model shows a trend of meticulous content and diversified objects.The crop growth model for small sample size has many advantages in low consumption,flexibility,efficiency,compatibility and so on.Besides,it can also make up for deficiencies in crop physiology,environmental stress and greenhouse control,in comparison with the major integrated model.But currently,to ensure the performance of model is the chief difficulties and significant of the crop growth models for small sample size.The paper mainly studies some key problems of crop model for small sample size,using the photosynthetic physiology and environmental monitored data of many crops.The chief content and result are listed as bellow.(1)Research on the preprocessing of monitored data.This paper used crop photosynthetic physiology and environmental monitoring system named PTM-48 A for continuous non-destructive monitoring of crops included strawberry,black beans,tomato,pumpkin,and cucumber.It adopted the cluster analysis to classify the measured time data,in order to obtain a continuous,comprehensive and uniform time period.Then the anomaly detection of data segment which meets the requirements for clustering,to detect and remove the outlier.The preprocessing based on cluster analysis and anomaly detection could offer a reliable sample data for the further analysis and modeling.(2)Research on factor analysis and selection.The study compared the performances of two multiple statistical analysis methods usually used in modeling,correlation analysis and path analysis,adopting the relation between CO2 exchange rate and the environmental influence factors.The analysis results showed that,compared with the correlation analysis,the path analysis was more suitable for crop models of small sample size.On the one hand,it could analyze more comprehensively,as it explained the direct and indirect effects between factors.On the other hand,path analysis was more efficient in variable selection,as it could eliminate the multicollinearity effect among variables.(3)Research on methods of modeling.The paper analyzed and compared two representative methods,regression model and artificial neural network model,which are commonly used in model fitting and construction.The experimental results of four common regression models showed that the pure quadratic regression model was more suitable for crop modeling of small sample size because of its higher accuracy and less complexity.On this basis,the forecasting model of CO2 exchange rate of the cucumbers with the use of GA-BP neural network was established.Compared with the pure quadratic regression and BP neural network,the GA-BP neural network was the more suitable model for crop model of small samples.