One of the most difficult tasks that both sugarcane producers and processors face every year is estimating the yields of sugarcane fields prior to the start of harvest. This information is needed by processors to determine when the harvest season is to be initiated each year and by producers to decide when each field should be harvested. This is particularly important in Louisiana because the end of the harvest season is often affected by freeze events. These events can severely damage the crop and result in decreased or negligible sucrose extraction at the sugar mill. Current methods to estimate crop yields rely on visual yield estimates and trend analysis of the yield histories of the fields in question. These methods are both subject to a high degree of variability. The objective of this research project was to determine if imagery obtained with UASs could accurately predict sugarcane yields prior to harvest. UAV imagery of a first-ratoon field of L 01-299 was acquired in August and November 2018. Imagery was also obtained in November from the same field in 2019 from the second-ratoon crop. The site for the study was located on a commercial sugarcane farm in Paincourtville, LA. Imagery was collected using a Sony Alpha 7, 12 megapixel true color sensor and a MicaSense, RedEdge multispectral sensor. The UAV platform utilized in these experiments was a Trimble UX5 fixed wing drone. Yield estimates were obtained in November, 2018 and 2019 by harvesting selected rows from the field in 30-m sections utilizing a chopper harvester and field wagon equipped with load cells. Sugarcane stalk samples collected during harvest were used to determine TRS at the USDA juice quality lab. Variogram analysis and block kriging were used to create yield maps obtained from the weigh wagon yield estimates. Orthomosaics of the acquired images were created using Pix4D and selected vegetation indices were derived from the orthomosaics. The image data was then correlated with field measurements of both cane and sugar yields. The image data was also compared to estimates obtained from a yield monitor mounted on the chopper harvester. Data from these evaluations suggest that UASs appear to have potential as an alternative to estimate sugarcane yields prior to harvest; however, additional research is needed to refine yield estimate procedures and increase the area covered by each flight.