How Can High Tech Solutions Help Deal with Challenges of the Wine Industry?

Artificial intelligence and machine learning could transform the winemaking sector, which has an old-school reputation but faces modern issues.

Fremont, CA; Economic globalization has created new trade routes and sparked a logistical revolution, which has resulted in massive technological advancements. In this setting, startups are more important since they are marketing themselves as innovation enablers for both large and small businesses. IoT, Big Data Analytics, and Blockchain are among the advances that can be applied to various fields, including the logistics of the entire wine supply chain.

Here's how the tech solutions can help face the challenges of the wine industry:

Offer drone checkups

Drones are at the center of many agricultural methods aimed at increasing the efficiency of farms, and wineries are no exception. Drones equipped with multispectral and thermal infrared sensors can detect signals on the vines that indicate their water status by taking detailed photographs as they fly by. It can also tell where disease or pests have spread throughout the vineyard, as well as which plants have died and need to be replaced.

Sorting out grapes

Some grape types are difficult to distinguish, but algorithms can help solve any Merlot or Grenache riddles by analyzing leaf images similar to those used to determine water stress and fertilizer status. The machine-learning algorithm predicts the cultivar with high accuracy of 94 percent and water stress with an accuracy of 88 percent using 13 shape and color variables as inputs.

Picking up the right grapes

Assessing the sugar level of a grape, which will be converted to alcohol throughout the winemaking process, is one way to determine whether it is ready to create a quality wine. However, it is more crucial to evaluate flavor and aroma generation, which is linked to the pattern of cell death in grapes, according to new studies. Winemakers can see in real-time in the field which grapes are ready to be picked and which aren't using a machine learning algorithm, eliminating the need to ship them to the lab.