1Department of Industrial Technology, California State University, Fresno 2255 E Barstow Ave, M/S IT 09, Fresno, CA, 93740-8002, USA
2Viticulture and Enology Research Center, California State University, Fresno 2360 E Barstow Ave, Fresno, CA, 93740-8003, USA
Segregation of wine grapes based on quality during harvest is a growing need for producers and wineries as spatial variability of vineyard quality is well established. While wine grape quality indicators like anthocyanin (mg/g) are measurable, there is no commercial technology to differentially harvest using such parameters. Geo-referenced field samples of wine grapes were measured for anthocyanin and brix using a portable near-infrared (NIR) spectrometer. Data was collected from 437 sampling vines in a 45 acre block and 1330 in a 160 acre block of vineyards in the San Joaquin Valley of California (2006–2007). Geo-spatial modeling of anthocyanin yielded quality zones of ‘high’ and ‘low’ quality while the brix dataset was utilized to determine the timing of the harvest. The anthocyanin concentration used to differentiate between high and low quality was based on cut off values of 0.87 and 1.05 mg anthocyanin/g fruit for the two vineyards specified by winemakers. A differential harvest attachment was developed for a commercial mechanical grape harvester that utilized the geospatial quality map for segregation of wine grapes on-the-go. Three 40 tons lots of wine grapes representing the standard (average) field blend, high anthocyanin and low anthocyanin were differentially harvested from each vineyard. These wine grapes were fermented separately and subjected to analytical and taste panel analysis resulting in significant (99.4% confidence) difference in wines produced
Precision agriculture, precision viticulture, differential harvest, anthocyanin, wine grapes, geo-statistics, kriging
