In agri-business, the market prices of crops are indicators for their demands and supplies. Therefore, crop price forecasting is a research of high interest, which can be used to detect potential selling points to increase the profit. For production management, these selling points can be deemed as recommended harvest time, and better cultivation schedules could be derived accordingly. In Taiwan, the Council of Agriculture (COA) establishes an official website that publishes crop prices from 19 local markets, which covers more than 100 different crops. This website is now widely referred by farmers and agri-enterprises as a channel to keep track of market information. Therefore, the published daily prices are good materials for research of data science. In this paper, we aim to investigate the spacial relationships between prices in different markets. The investigation is accomplished by examining the forecasting price given by four well-known spacial algorithms, which are the nearest neighbor (NN), the inverse distance weighting (IDW), the Kriging method, and the artificial neural network (ANN). We compare the performance of these four algorithms with the price data obtained from 15 markets on the official website of COA. The experimented crops are cabbage, bok choy, watermelon, and cauliflower. According to the experimental results, Kriging achieves the lowest error in percentages. Considering the time efficiency, the Kriging method is also recommended for the development of forecasting service, since the regression can be accomplished efficiently.
['Yung-Hsing Peng', 'Chin-Shun Hsu', 'Po-Chuang Huang']
An investigation of spacial approaches for crop price forecasting in different Taiwan markets