|Publication type:||Conference other|
|Type of review:||Not specified|
|Title:||Luftaufnahmen zur Optimierung logistischer Herausforderungen bei der Traubenernte|
|Conference details:||62. Deutscher Weinbaukongress, Stuttgart, Deutschland, 27.-30. November 2016|
|Subjects:||Weinberg; Fernerkundung; Prognosemodell Traubenreife|
|Subject (DDC):||634: Orchards, fruits and forestry|
|Abstract:||Since 2013 the ZHAW Wädenswil cooperates with the Hessisches Staatsweingut Kloster Eberbach sampling two vineyards (7ha) with different trellis systems (guyot and minimal pruning) to evaluate possibilities for picture based quality assessment. Berry samples are analyzed for total solids [°Oe], total acidity [g*L-1] and pH using FTIR technology. To gain detailed information about the ripening process three sampling dates each season are conducted. For each date pictures are taken in four different wavelengths (red, green, red edge, nir) of both vineyards using a UAV (unmanned aerial vehicle) from 60-100 m. The sampled vines are georeferenced and spatial data was interpolated using kriging-method. To extract Inter-annual spatial variation of grape quality, vines were split into clusters of high-performers, average-performers and low-performers and their ripening performance was analyzed within one season. Normalized differenced vegetation index (NDVI) was computed to compare picture based zoning with ground-truthing data. Within a season min/max difference of total acidity was found to diminish while min/max difference in total solids remains stable during ripening season. Inter-annual spatial variation of grape quality was found to be mainly unstable with only small exceptions. Depending on the year, fluctuations between clusters were astonishingly high leading to a false interpretation as absolute values do not represent future performance. For example, in the year 2013 only 58% (2014 43%, 2015 67%) of vines in the cluster of high-performers could hold their status till next sampling date two weeks later. During this time, the average performance [°Oe*day-1] was even lower than compared to the cluster of low-performers (2013 high-performers: 0.38 [°Oe*day-1], low-performers: 0.81[°Oe*day-1]).|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Food and Beverage Innovation (ILGI)|
|Appears in Collections:||Publikationen Life Sciences und Facility Management|
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