Plant diseases cause an annual global crop loss of 20-40%, leading to estimated economic losses of 30-50 billion dollars. Tomatoes are susceptible to more than 200 diseases. Breeding disease-resistant cultivars is more cost-effective and environmentally sustainable than the frequent use of pesticides. Traditional breeding methods for disease resistance, relying on direct visual observation to measure disease-related traits, are time-consuming, inaccurate, expensive, and require specific knowledge of tomato diseases. High-throughput disease phenotyping is essential to reduce labor costs, improve measurement accuracy, and expedite the release of new varieties, thereby more effectively identifying disease-resistant crops. Precision agriculture efforts have primarily focused on detecting diseases on individual tomato leaves under controlled laboratory conditions, neglecting the assessment of disease severity of the entire plant in the field. To address this, we created a synthetic dataset using existing field and individual leaf datasets, leveraging a game engine to minimize additional data labeling. Consequently, we developed a customized unsupervised domain-adaptive tomato disease segmentation algorithm that monitors the entire tomato plant and determines disease severity based on the proportion of affected leaf areas. The system-derived disease percentages show a high correlation with manually labeled data, evidenced by a correlation coefficient of 0.91. Our research demonstrates the feasibility of using ground robots equipped with deep-learning algorithms to monitor tomato disease severity under field conditions, potentially accelerating the automation and standardization of whole-plant disease severity monitoring in tomatoes. This high-throughput disease phenotyping system can also be adapted to analyze diseases in other crops with similar foliar diseases, such as maize, soybeans, and cotton.