Agricultural Product Quality Supervision And Traceability System Design

With the improvement of living quality,security consciousness of agricultural products quality becomes higher and higher.Due to the complicated types,safety factors and circulation of gricultural products,it is very important to establish the whole process of regulation and traceability.The rapid development of big data technology provides a new platform for the quality supervision and traceability system of agricultural products.On the basis of analyzing domestic and foreign quality supervision and traceability system technologies of agricultural products,an agricultural product quality supervision and traceability system is set up based on Hadoop platform with big data technology.Based on the deep analysis of support vector machine and BP neural network,the regional quality supervision and forecast model of agricultural products is established base on SVM.When choosing the penalty factor and kernel function parameter,the pesticide pollution index and heavy metal pollution index in the original data are divided into K groups.Each group of data is used as a validation set,and the remaining K-1 group data is used as the training set.The average classification precision of validation set is used as the final cross validation accuracy of the classifier.The penalty factor and kernel function parameter corresponding to the maximum precision is used to train the model,realizing the prediction of agricultural products region that needs to be supervised.Compared with BP neural network,the classification accuracy of SVM has improved by 10 percent.In order to realize the prediction of agricultural products corruption rate data,the timing quality prediction model of agricultural products was constructed based on SVR.The corruption rate of agricultural products were divided into two groups.The selection method of optimal penalty factor and kernel function parameter is same as the previous model.The model is trained with the first group of data and predict the data with the second original data,analyzing absolute error and relative error.Compared with BP neural network,the SVR algorithm improved nearly 5 percent on correlation coefficient between the predicted value and the true value and is closer to the original data.Agricultural products quality supervision and traceability system is designed from Web and Android.The test environment is built.The main functions of the Hadoop supervision traceability platform,Web and Android end are tested.The test results show that the system has certain practical value in agricultural product quality supervision and traceability.