Support of decision making by data mining using neural system

In this paper, the environmental factors surrounding fruit trees are used as the explanatory variables, and the factors concerning the quality of the agricultural produce are used as the explained variables. The observed data are analyzed. The object of analysis is the Onshu orange (mandarin orange). The purposes of this paper are analysis of the effect of environmental factors on product quality, the target value settings for the controllable environmental factors in order to produce high-quality products, and evaluation of the cultivation environment for each item. A system for data analysis is proposed which extracts the useful information for the decision-making such as cultivation management, as well as harvest/shipping planning, from the observed data. The system uses a probabilistic neural network and learns the mapping from the describing variables consisting of observed data to the explained variables. By superposing the acquired knowledge, inverse modeling can be performed. It is shown that by using the proposed system, multivariate nonlinear inverse mapping can be realized instead of the linear inverse regression estimation used in conventional single regression analysis. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(11): 102–110, 2005; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/scj.10577