Journals Proceedings

Abstract

The agricultural issue is always the critical factor to achieving the sustainability of a country. Due to the growing of plants is affected by weather, season, and lots of external influences, the harvest of crops is unstable and might cause the imbalance between supply and demand in the market. Therefore, the precise prediction of demand in the crops transaction market is important. It is helpful to the farmers to make the cultivating plan and also beneficial to the development of agriculture. In our research, we apply back propagation neutral network (BPNN) to develop a time-series prediction model. The model is used to predict the trading volume of cherry tomatoes in the fruit transaction market in Taiwan. The trading volume indicates the demand in the market, for that reason, the farmers and government can make cultivating plan effectively by the prediction results. We collected the transaction information of cherry tomatoes from 2011 to 2014. The transaction information is used to train the BPNN model and evaluate the accuracy of prediction. The analysis results show we models have higher than 80% accuracy rate. It implicates that BPNN can be used to predict the trading volume of crops in the market.