Design And Validation Of Discriminating And Tracing System Of Soybean For Mechanized Operation

Heilongjiang province is the main soybean production region in China,which accounts for more than 30% of the national soybean production amount.However,problems such as the imperfect Origin Traceability Technology and the lack of brand protection technology are still exist.In the process of soybean production,whole process mechanization for the tillage and soil preparation,planting and fertilizing,field management and harvesting are necessary conditions for the standardization of soybean origin traceability.In order to solve these problems,soybean and soil samples from Beian Administration Bureau of Heilongjiang Farms and Land Reclamation Administration,and Nenjiang Northern Agricultural Development Co.Ltd of COFCO were taken as the research objects,and the organic composition and mineral element contents of soybean and soil samples from the two areas were determined through 2015 and 2016.By using Variance analysis,Principal component analysis,Cluster analysis and Discriminant analysis,3 organic components and 7 mineral element contents were selected as the traceability characteristic indexes.An soybean origin discriminant model was built,and combined with Dimension specification for linear discriminant analysis,the Discriminant system of soybean origin was developed based on Support vector machine algorithm.Results and conclusions were listed as following:(1)Four organic components contents(protein,fat,ash and soluble sugar)from 97 soybean samples from the experimental fields of two main soybean production areas were determined through 2015 and 2016.Statistic methods including Variance analysis,Principal component analysis,Cluster analysis and Discriminant analysis were used to explore the characteristic indexes of organic components for soybean origin traceability.The results indicated that the Overall correct discriminant rate and Overall correct cross-validation rate for soybean origin reached 86.0% based of the organic component discriminant model.(2)113 soybeans and soils samples from the experimental fields of two main soybean production areas through 2015 and 2016 were selected as the research objects.52 minerals elements from soybean and soil were determined,and Statistic methods including Variance analysis,Principal component analysis,Cluster analysis and Discriminant analysis were used toexplore the characteristic indexes of minerals elements for soybean origin traceability.The results indicated the Overall correct discriminant rate for two soybean origin reached 93.2%based on the 8 mineral elements(Mg,Mn,Sr,La,Gd,Tb,Hf,Ti,and other mineral elements)discriminant model.(3)112 soybeans and soils samples from the experimental fields of two main soybean production areas through 2015 and 2016 were selected as the research objects,and 56 random samples in 2014 were verification objects.Seven mineral elements including Mn,As,Sr,La,Nd,Tb,Hf,and three organic component,including protein,fat,and soluble sugar were selected as the characteristic index,and Fisher discriminant model was built for soybean origin traceability.According to the Dimension specification of linear discriminant analysis and Support vector machine(SVM),the discriminant rate of soybean origin was 94.6%,which was better than92.9% of the linear discriminant model.(4)The model based on MVC system was built on EF web page framework,the database for mineral elements and organic components of soybean was established,and the discriminant system for soybean origin based on Support vector machine algorithm was developed.By using the system for soybean origin discrimination,the correct discrimination rate was 97.5%,which was higher than 90% of the linear discriminant model.The established of the discriminant system for soybean origin traceability has a relatively high discrimination rate,which lays a theoretical foundation for the future development of soybean origin traceability.