Study On Multi-index Wheat Quality Prediction Method Based On Machine Learning

As one of the most important food crops in China,the storage safety of wheat is of great significance to ensure the stable development of society and the healthy life of residents.Predicting wheat quality is helpful to understand the deterioration trend and plays an important role in ensuring the storage safety of wheat.The aging and deterioration of wheat quality is accompanied by the numerical changes of physiological and biochemical indexes.Predicting the variation trend of multi-index data is helpful to detect the change of wheat quality in time and reduce the storage loss.Aiming at the shortcomings of the existing wheat quality prediction methods in the aspects of accuracy,stability and reliability,the machine learning algorithm was selected in this paper to explore the similarity and difference of the change rules of multiple physiological and biochemical indexes,it realized the quantitative analysis of the contribution of wheat multi-index weight,the accurate prediction of single index time series change,the stable prediction of multi-index overall rule,and the quantitative prediction of wheat quality state category,which can provide some theoretical and technical support for the accurate prediction and analysis of wheat storage quality.The main innovations of this paper are as follows:(1)Study on the calculation method of wheat multi-index weightsIn view of the existing research work that rarely involves the complex effects among multiple indexes,a method for calculating the weight of multiple indexes of wheat based on the information entropy model was obtained in this paper.This paper was based on the conditional entropy quantitative calculates the information obtained by a single wheat index form other indexes,and weights were assigned to the differential effect of multiple indexes of wheat according to the amount of mutual information,which can laid a foundation for predicting wheat quality accurately.(2)Study on time series prediction of single index of wheat qualityIn order to improve the accuracy of single index prediction of wheat quality during different storage periods,a long and short memory generation adversarial network model was proposed in this paper.The model was based on the improved long short-term memory network to obtain the time series characteristics of a single index,and by extracting the data change characteristics of other indexes based on generative adversarial network to further improve the prediction performance of the model,so as to accurately predict and analyze different indexes of wheat quality.(3)Study on the prediction of multi-index wheat overall qualityFor the problem of insufficient information and low stability of single index data in predicting wheat quality due to the complex mechanism of wheat quality deterioration,a width-adaptive improvement algorithm model was constructed based on multi-index.In this model,a feature learner and an enhancement learner were obtained through width feature transformation of multi-index data sets,and the weighted combination of the Adaboost algorithm was used to make a stronger learner with better performance,and then the change law of the whole wheat quality was predicted and analyzed.(4)Study on classification prediction of wheat quality statusOn the basis of the time series prediction of wheat quality indexes and the analysis of the overall characteristics of multiple indexes,further research is carried out in this paper.In order to achieve the quantitative analysis and accurate prediction of different quality states of wheat during storage,a semi-supervised wheat quality category prediction model was proposed in this paper.In this model,the improved twin support vector machine and density peak clustering algorithm were used to classify the quality state of wheat storage,and a semi-supervised collaborative classification algorithm was used to realize the accurate prediction and analysis of the multi-index wheat storage quality states categories.