Buffelgrass stands out as a key forage species in the semi-arid regions of Brazil, particularly in northern Minas Gerais, due to its capacity to increase pasture productivity and enhance animal performance. Seed viability is traditionally assessed using the tetrazolium test, a reliable but time-consuming and manual method. In this context, computational techniques such as the YOLO framework - a state-of-the-art algorithm for object detection and classification - have shown great potential in seed analysis. This study proposes the classification of buffelgrass seed vigor levels through image-based analysis using version 11 of the YOLO framework. Four seed lots were evaluated through tests of germination, first count, seedling emergence, accelerated aging, and tetrazolium analysis. Mean values were compared using the Tukey test at 5% significance level. In addition, Pearson's correlation coefficients were calculated among the tests. The seeds were categorized into three vigor levels based on the results obtained from the tetrazolium test, and four classes for training the classification model. For model training, the YOLOv11 algorithm was employed, using digitized laboratory images that underwent feature extraction, balancing, and data augmentation procedures. Model performance was evaluated using top-1 accuracy, as well as analyses of training and validation losses. The model showed excellent performance, achieving 100% accuracy in testing, confirming the potential of this computational approach.