Vineyard management faces unique challenges, particularly in disease monitoring and treatment, where timely and accurate assessments can mitigate yield losses and reduce chemical inputs. This dissertation presents an in-field autonomous robotic system designed for vineyard disease scouting and adaptive management. Focusing on diseases including Downy Mildew, Powdery Mildew, and Grapevine Leafroll-associated Virus, the system combines advanced stereo vision, strobe light enhanced imaging, and real-time processing to detect and assess disease severity at the leaf level. The robot autonomously traverses through the field, identifies and classifies symptomatic leaves, and estimates the disease severity. A core aspect of this research is the system’s capability to generalize disease detection models across seasons, addressing variability in vineyard conditions and enhancing reliability in large-scale deployments. Comparisons between robotic and traditional human scouting reveal the effectiveness and potential scalability of this technology, with the robotic method demonstrating improvements in detection consistency. Furthermore, by recording high-resolution disease severity data, this system contributes valuable ground-truthing information for large scale, remote sensing based disease detection. The findings from this work emphasize the role of autonomous disease scouting in modern viticulture, offering insights that extend to other perception tasks in vineyards, such as bud, shoot, berry, and weed detection. By providing growers with real-time, leaf-level insights, this technology represents a significant advancement in precision viticulture, reducing chemical inputs, improving crop health, and supporting sustainable vineyard management practices.