A fuzzy inference system to model grape quality in vineyards

Fuzzy inference systems (FIS) are particularly suited for aggregating multiple data to feed multi-variables decision support systems. Moreover, grape quality is a complex concept that refers to the simultaneous achievement of optimal levels in many parameters, thus single berry attributes spatial data are not adequate to define grape suitability for a specific end use. The aim of the present study was to develop and validate a FIS to classify grape quality based on selected grape attributes in a commercial vineyard in Central Greece planted with Vitis vinifera cv. Agiorgitiko, during 2010, 2011 and 2012. The vineyard was sectioned in 48 cells sized 10 x 20 m; total soluble solids, titratable acidity, total skin anthocyanins and berry fresh weight were measured at harvest on the same grid and were used in the FIS as inputs to build linguistic rules based on expert knowledge. The result of the FIS was a numerical value (Grape Total Quality, GTQ) which corresponded to a fuzzy set of grape quality classes (very poor, poor, average, good, and excellent). The validation process for the proposed FIS consisted of two parts: a comparison of GTQ with an independent set of data by viticulture experts and a comparison with soil and grapevine properties to verify its spatial relevancy. The evaluation process showed high general agreement between GTQ and expert evaluation suggesting that the FIS was able to model expert knowledge successfully. Moreover, GTQ exhibited higher variability than the individual grape quality attributes in all years. Among individual grape components, anthocyanins and berry weight seemed to be more important in determining GTQ than total soluble solids and titratable acidity. According to the results, FIS could allow the aggregation of grape quality parameters into a single index providing grape growers with a valuable tool for classifying grape quality at harvest