Algorithms Enter the Vineyard

Artificial intelligence is becoming a key part of the winemaking process, from the vineyard to the final sale. The industry is facing new challenges, including unpredictable weather and disease outbreaks. According to the International Organisation of Vine and Wine (OIV), 2024 saw global wine production drop to 225.8 million hectoliters, a 4.8% decrease from 2023. The total area of vineyards also shrank to 7.1 million hectares, down 0.6% from the previous year. These changes are making prediction, optimization, and automation more valuable than ever for wine producers.

Precision viticulture is one area where AI is making a difference before grapes even reach the winery. The OIV defines precision viticulture as a data-driven approach that uses technology for site-specific decisions to improve production. Tools like soil and weather sensors, satellite imagery, drones, GPS mapping, and robotics are now common in vineyards. AI helps turn the data from these tools into practical advice, such as where to spray pesticides or how much water to use.

Commercial pressures are also pushing wineries to adopt digital solutions across their operations. A 2024 survey by ProWein and Geisenheim University found that while many businesses see the value in e-commerce and customer relationship management, only 15% plan to invest in new digital marketing tools by 2025. Data management remains a challenge for many wineries, which can slow down AI adoption.

Regulation is another factor shaping how AI is used in wine. In the European Union, the AI Act took effect on August 1, 2024. While most vineyard and winery uses of AI are not considered high-risk under this law, companies still need to follow rules on documentation, transparency, and data governance.

In the vineyard, AI adoption focuses on monitoring conditions and making targeted interventions. Modern vineyards use a mix of ground sensors, weather stations, and remote sensing from satellites or drones equipped with advanced cameras. A review published in Horticulturae covering research from 1999 to 2022 highlights that these technologies generate large amounts of data that require machine learning to interpret. AI is increasingly used for early detection of diseases like downy mildew. For example, drone-based multispectral imaging can spot changes in grapevine leaves that signal infection before it spreads widely.

Deep learning models are now fast enough for real-time disease detection in the field. One study reported a model that could identify downy mildew with nearly 90% accuracy at speeds suitable for practical use in vineyards. AI is also being used to score grapevine resistance to disease for breeding programs, with some neural networks achieving over 80% accuracy—much faster than manual methods.

Yield prediction is another area where AI shows clear benefits. Accurate forecasts help wineries plan labor needs, harvest timing, and tank space. Research published in Precision Agriculture demonstrated that computer vision models could predict grape yields up to two months before harvest with strong accuracy across multiple grape varieties. Advances in smartphone technology mean that even low-cost devices can be used for yield estimation by combining color and depth sensors with machine learning algorithms.

Australian research groups have reported improvements in yield prediction by combining ground-level cameras and LiDAR with aerial imagery, resulting in an 8% increase in predictive accuracy at the block level for vineyards in Australia and California.

Water management is another critical application for AI as droughts become more common and water costs rise. Machine learning models can now predict water stress at the level of individual vines using data on terrain, soil conductivity, and plant health indices collected by sensors or drones. These predictions help guide precision irrigation strategies.

AI also supports targeted pesticide spraying by analyzing canopy structure and disease risk to create variable-rate application maps. Field trials have shown that these systems can maintain disease control while reducing chemical use and water consumption compared to traditional constant-rate spraying.

Robotics brings together AI with physical automation in the vineyard. Companies like Vitibot are marketing autonomous electric vehicles designed for narrow vineyard rows using high-precision GPS navigation. These robots address labor shortages and improve safety by handling repetitive tasks such as spraying or mowing.

As AI becomes more integrated into winemaking operations, its role continues to expand beyond production into sales and consumer engagement. However, challenges remain around data integration, regulatory compliance, and investment priorities within a fragmented industry landscape. The ongoing evolution of both technology and regulation will shape how quickly—and how widely—AI transforms winemaking in the years ahead.