# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Automated Grape Picking Systems  
AI-driven automated grape picking systems represent a significant advancement in viticulture. These systems integrate several advanced technologies: robotics for picking and handling grapes, computer vision for assessing grape clusters, and machine learning for processing visual data to make harvesting decisions. This combination allows grapes to be selected at their optimal ripeness [58]. Similar to UAVs, robotic harvesters are equipped with cameras and sensors that capture detailed images of the grape clusters. These high-resolution images are analyzed in real time by AI algorithms, which assess parameters such as color, size, shape, and texture to distinguish between ripe and unripe grapes [58]. This selective harvesting improves the overall quality of the wine by reducing the number of unripe grapes picked [59]. Advanced AI models, including those based on deep learning, have shown high accuracy in identifying ripe grapes even under challenging conditions, such as varying light levels and leaf occlusion [47]. Additionally, GPS technology and mapping systems ensure efficient navigation of the vineyard, covering all areas without redundancy [60]. Recent advancements in sensor technologies, such as the GA-YOLO model, have significantly enhanced the ability of robotic systems to detect and harvest grapes in dense and occluded environments [58]. The integration of advanced kinematic models and real-time data processing ensures high accuracy and efficiency, even in complex vineyard layouts [61]. The impact of automated grape picking systems on vineyard operations is profound. By increasing the speed and efficiency of harvesting, these systems allow vineyard managers to maximize output during the harvest season. The precision of AI-driven harvesters ensures that grapes are picked at their peak, enhancing wine quality and reducing waste from overripe or damaged grapes [59]. Automated systems also contribute to more sustainable practices by reducing the need for chemical treatments and minimizing the environmental impact of harvesting operations. Precise targeting of ripe grapes reduces waste and ensures more efficient use of resources [62]. These automated systems offer several advantages over traditional manual harvesting. They can operate continuously, day and night, increasing efficiency and reducing labor costs. Automation ensures that grapes are picked at their precise moment of optimal ripeness, which is crucial for producing high-quality wine [63]. Studies have shown that these systems can reduce labor costs by up to 40% while maintaining or improving grape quality [64]. Additionally, they enhance safety by reducing the need for human workers to perform repetitive, physically demanding tasks in potentially hazardous conditions, such as steep slopes or extreme weather [60]. Several vineyards have successfully implemented these systems. A vineyard in France reported a 25% increase in harvesting efficiency and significant improvements in grape quality after adopting robotic harvesters [64]. Many studies have focused on the application and improvement of the GA-YOLO model. [PERSON] et al. introduced a lightweight YOLO model to detect dense and occluded grape targets using drones in Bagui Garden, Guangxi, China. This model achieved an average accuracy (mAP) of 96.87% and a detection speed of 55,867 FPS, significantly reducing model parameters by 82.79% [58]. In addition, [PERSON] et al. developed a lightweight-improved YOLOv5s model for grapefruit and stem recognition to enhance mechanized harvesting, achieving a precision of 96.8%, recall of 97.7%, and mAP of 98.6% [45; 59]. In another work, [PERSON] et al., developed a robust accurate method for the detection and localization of the peduncle of table grapes based on a combination of instance segmentation and monocular depth estimation using Convolutional Neural Networks (CNN), with direct implementation in automatic grape harvesting with robots [65].