# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Disease Prediction Models  
Disease prediction models are among the most impactful applications of AI in viticulture. These models utilize machine learning algorithms to analyze historical data, weather conditions, and environmental factors to predict the likelihood of disease outbreaks. Effective disease management is critical for maintaining vineyard health and productivity, and AI provides advanced tools to enhance this process. Data collection is the foundational step in developing effective disease prediction models. Various sources, including weather stations, soil sensors, and satellite imagery, provide crucial information about environmental conditions that affect vine health. Sensors placed throughout the vineyard monitor factors such as humidity, temperature, soil moisture, and leaf wetness, which are essential for the development of diseases like powdery mildew and downy mildew [37]. Machine learning models such as Support Vector Machines (SVM), Neural Networks, and Random Forests are employed to identify patterns and correlations that are not immediately apparent [14]. These models are trained with historical data to recognize conditions that typically precede disease occurrence, enabling early warnings and proactive management [38]. Recent studies have shown that using data from inside the grape canopy, as opposed to external weather stations, significantly improves the precision of forecasting models by capturing microclimatic variations critical for disease prediction [39]. Moreover, the integration of IoT technology with AI has further enhanced the data collection process. IoT sensors deployed in vineyards continuously collect and transmit real-time data on environmental parameters to cloud-based platforms for analysis. This approach not only improves the accuracy of disease prediction but also enables timely and precise interventions [40]. Predictive Modeling and Implementation  
Predictive modeling involves creating algorithms that can forecast disease outbreaks based on real-time data inputs. For instance, a model might predict the likelihood of a powdery mildew outbreak by analyzing recent weather patterns, current humidity levels, and historical outbreak data. This predictive capability allows vineyard managers to implement targeted interventions before a disease becomes widespread [41]. Implementing these models involves integrating them with decision support systems that provide actionable insights to vineyard managers. For example, a decision support system might send alerts when conditions are favorable for disease development, recommending specific actions such as adjusting irrigation schedules or applying fungicides. This proactive approach reduces the reliance on routine chemical applications, promoting more sustainable farming practices [42]. Recent advancements in integrating IoT devices and machine learning models have further enhanced these systems, allowing for real-time monitoring and decision-making [40]. The efficacy of AI-powered disease prediction models in viticulture has been demonstrated by several case studies. For instance, research conducted in Tuscany (Italy), demonstrated how the Etat Potentiel d'Infection (EPI) current model accurately predicted the risk of downy and powdery mildew infections, enabling vineyard managers to reduce fungicide applications by up to 50-77% for powdery mildew and 42-50% for downy mildew, without compromising crop protection [41]. Another study conducted by [PERSON] et al. developed and tested predictive models for grape downy mildew (GDM) in Bordeaux vineyards. The research utilized a dataset spanning nine years and incorporated both statistical and machine learning algorithms, including generalized linear models, LASSO, random forests, and gradient boosting, to predict the probability of high incidence and severity of GDM. These models used real-time data on disease onset dates and average monthly temperatures and precipitation.