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The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

The purpose of this paper is to provide further insight into Chinese wine consumers’ preference, grasp the regional sensory preference differences of China and build up a predictive model for wine consumers’ sensory preferences.

The study involved 3,421 Chinese wine consumers in the survey. Classified statistics were conducted to excavate regional differences of wine consumers’ sensory preferences. By analyzing influencing factors, prediction models for consumers’ sensory attribute preferences were constructed on the basis of multivariate disorder logistic regression method.

Empirical research showed that the wine with the following sensory attributes was the most preferred by Chinese consumers: dry red, refreshing and soft taste, still type, moderate aroma degree and mellow aroma, and sweet wine was also popular. Consumers’ preference varied from region to region. The proposed predicting method of the study realized more than 70 percent accuracy when conducting prediction for color, sweetness, aroma type and flavor preferences.

By shedding light on the latest sensory attribute preferences of Chinese wine consumers, this study will help wine industry participants conduct market segmentation based on the diversification of consumers’ preferences. The wine enterprises can gain guidance from the results to conduct market positioning, adjust strategies and provide specific production for target wine consumers.

Based on the actual situation of Chinese wine market, this study defines sensory attribute indexes of wine from the perspective of wine consumers and presents the most recent comprehensive research on the sensory preferences of Chinese wine consumers through a nationwide survey.

Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.

In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.

The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.

This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.