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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 108-117.doi: 10.12133/j.smartag.2020.2.3.202008-SA003

• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles     Next Articles

Short-Term Price Forecast of Vegetables Based on Combination Model of Lasso Regression Method and BP Neural Network

YU Weige1,2(), WU Huarui1,2,3(), PENG Cheng1,2,3   

  1. 1.Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3.Key Laboratory of Agri-informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
  • Received:2020-08-13 Revised:2020-09-23 Online:2020-09-30


Vegetables are an important part of residents' diet. The abnormal fluctuation of vegetable prices has caused losses to the economic interests of vegetable farmers and also affected the daily diet and quality of life of residents. However, there are some difficulties in vegetable price prediction, such as large price fluctuation and complicated influencing factors. Cucumber is the main category of vegetables and a common food on the daily table of residents and its recent price fluctuations have aroused widespread concern. In this research, taking cucumber as the research object, a combination model (L-BPNN) combining Lasso regression method and BP neural network was constructed to forecast the short-term price of cucumber. Firstly, the factors affecting the price of cucumber, such as supply, demand and circulation were analyzed. Then the price fluctuation characteristics of cucumber in China from 2010 to 2018 were analyzed and 24 factors were selected as the influencing factors of cucumber price. In the case of complex factors, Lasso regression was used to compress the 24 input influencing factors and the 12 remaining influencing factors with large correlation degree after compression were used as the input influencing factors of BP neural network. Among the 12 related factors , the positive effects included: land cost, per capita disposable income of urban residents, urban vegetable consumption price index, fuel surcharge, booth fee, packaging and processing fee, inflation rate, affected area and temperature deviation from normal value; negative effects included sown area, industrial support amount and average temperature. On this basis, a combination model combining Lasso regression method with BP neural network (L-BPNN) was constructed to forecast the short-term price of cucumber. The neural network was used to train and adjust the model between the input influencing factors and the output price. Compared with the regression analysis and intelligent analysis methods, the results show that the average relative error of L-BPNN combination model was the smallest, only 0.66%, which was 64.52%, 82.11% and 86.2% lower than Lasso regression model, BP neural network model and RBF neural network model respectively, and had higher prediction accuracy. The results of this study realizes the short-term price forecast of cucumber, and can also be extended to other vegetable varieties, which is of great significance for guaranteeing the income of vegetable farmers and stabilizing the market price of vegetables.

Key words: vegetable, influencing factors, price forecast, combination model, Lasso regression, BP neural network, RBF neural network

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