The main bottleneck to accelerating the development of new sugarcane varieties with desirable traits to meet the demands of the sugar-energy sector and adaptation to climate change is the absence of high-throughput phenotyping methods for evaluating varieties in the field. Traditional methods of field phenotyping depend on trained specialists for visual evaluations that are slow, laborious, and subjective. In this study, we investigated UAV-based multispectral data and machine learning algorithms to improve efficiency in the evaluation of field phenotyping of sugarcane varieties regarding the resistance to infection by orange and brown rusts. Spectral data from five bands (Blue, Green, Red, Red-edge, and NIR) and 14 vegetation indices were tested in direct correlations with infection scores collected in the field for the two types of rust. Sugarcane varieties were classified according to their resistance to rusts using three machine learning algorithms (Random Forest, radial SVM, and KNN). Correlations between the Red band data and infection scores of the two types of rust were significant (r = 0.67) for evaluations made at 165 days after planting (DAP). Conversely, regarding the varietal classification into three resistance classes, a high level of overall (88.1%) and balanced (Resistant = 90.3, Moderately resistant = 88.6, and Susceptible = 82.1) accuracy was reached at 195 DAP with the radial SVM model. UAV-based multispectral data is able to assist in the phenotyping of new sugarcane varieties regarding the resistance to these diseases.