The Research Of The Soil Moisture Monitoring And Forecasting System Based On IOT

The internet of things(IOT) is regarded as the third wave of information industry. Many scholars are strongly interested in IOT and join the ranks of its research. In this thesis, in order to improve the scientific level of cultivating strawberries, the internet of things technology has been applied to the cultivation of strawberries, which will reduce the waste of resources and increase the yield of the crop.Herein, for obtaining the real-time soil moisture information of strawberry fields, the soil moisture monitoring system based on IOT is set up to monitor growing environment of strawberries. In this system, the outliers may be collected by a part of sensors in the process of gathering soil moisture information. In order to avoid this possible situation affecting the subsequent data processing, the correlation function of fuzzy mathematics is joined this system to preprocess the data. It is found that the expected results can effectively be achieved by using the proposed algorithm.After completion of the data acquisition, the soil moisture data needs to be analyzed to find out the relationship between the parameters and obtain the mutual influence between parameters. Here, the analysis method can mainly be summarized as the mathematical statistical analysis method and the grey system theory analysis method. The mathematical statistical analysis method contains correlation analysis, regression analysis, cubic exponential smoothing method and linear twice moving average method. The grey system theory analysis method includes GM(1,1) model, the overall correlation analysis and grey correlation analysis. The soil moisture data are analyzed using the above methods to find out the influence of the relationship between parameters on the cultivation of strawberries.In the practical production, in order to avoid unnecessary waste of human resources, the soil moisture forecasting system need to be established. In this thesis, the support vector machine method is used to establish the prediction model. In order to improve the prediction effect of the model, the parameters of the established model are optimized by the bacterial foraging optimization algorithm. To avoid the influence of human factors, the collected data is analyzed to determine the input parameters by using grey correlation analysis method in the process of selecting the input parameters. Then, the model is trained and its prediction effect is tested. If the expected performance has been achieved by the prediction model, the model will be used to forecast the soil moisture.The ultimate goal of the above analysis is that farmers are willing to use it. However, farmers are only concerned about the analysis results in application. Therefore, C# programming language is used to design the network monitoring platform based on WEB. The monitoring platform can facilitate the users to view data, such as the real-time data, the historical data,the analysis results and suggestions of production of soil moisture in growing region.After a period of operation, the system is stable and reliable. This system can realize the function of design and meet the basic requirements of users.