Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

Yield monitoring can provide the winegrowers with information for precise production inputs during the season, thereby, ensuring the best possible harvest. Yield estimation is currently achieved through an intensive process that is destructive and time-consuming. However, remote sensing provides a group of proximal technologies and techniques for a non-destructive and less time-consuming method for yield monitoring.The objective of this study was to analyse three different approaches, for measuring grapevine yield close to harvest.

The past three decades of terrestrial remote sensing research have delivered unprecedented insights into our fundamental ability to detect, quantify, and differentiate plant disease (Gold 2021). However, much of our fundamental knowledge in this domain has come from studies in non-agricultural systems and until recently, most agricultural studies, when extant, have focused on tree crops where canopy closure and large plot and plant size facilitate stress detection at low spatial resolution. Recent engineering innovations and advancements in constellation architecture design have refined the accuracy and scalability of airborne and spaceborne sensing platforms, enabling us to monitor diverse specialty crops, including grapevine, planted in smaller, spatially varied fields.

Aims: The aims of this study were to i) characterize the impact of geographical origin and viticulture treatments on Chilean Cabernet-Sauvignon, and ii) develop machine learning models to predict its quality. 

Hail damage can have a major impact on the vine’s physiological growth (defoliation, wood and cane damage) and can lead to significant yield and economic losses.

The wide diffusion of organic cultivation of vineyards and the need to reduce the use of pesticides highlights the urgent need for alternative and sustainable methods of vine protection by pathogen molds.