Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

Winemaking is a complex microbial process involving the co-existence and interactions of various microorganisms [1].

Eleven wine-growing sites are now on the UNESCO World Heritage List as Cultural Landscapes. If the viticultural character of these sites constitutes the main argument for the demonstration of their heritage value, the terroir and its biophysical and environmental characteristics tend however to appear in a minor mode compared to the aesthetic and cultural dimensions. In other words, the “specific characteristics of the soil, topography, climate, landscape and biodiversity” (OIV definition) are most often used as descriptive elements in the presentation of the sites, but it is more the aesthetic, historical,

Los estudios regionales presentaron en Geografía, como en otras ciencias, en este siglo, varios enfoques. Cualquiera que sea la mirada sobre el espacio, en la base de la temática regional está la concepción

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.

Environment features and management operations on shoot and leaves modify the canopy during the vegetative season, changing the grapevine microclimate and the ratio between photo synthetic sources (the canopy) and productive sinks (the grapes).