In much of the world, sugarcane is planted in a mechanized fashion using billets, which are shorter segments of cane harvested and cut by a combine harvester. The mechanized harvesting process can damage billets, which introduces pathways for disease, and overall reduction of billet quality. Compared to whole stalk planting with manual methods, growers must approximately double the planting density when using billets. As a first step toward improving sugarcane production with robotics technologies, this letter presents the analysis of sugarcane billet quality using computer vision. A large sample of sugarcane billets was harvested at a research farm in Houma, LA, USA. A group of crop scientists and growers then categorized the billets into six classes of damage according to physical features visually evident. To better understand the correlation between the type of damage and sugarcane germination, we planted 120 samples from each class in test plots and then recorded plant emergence rates. A data set of billet imagery was collected with charge-coupled device and stereovision sensors in both outdoor and indoor lighting conditions. Offline image processing resulted in approximately 90% successful classification of sugarcane billet damage.