Sugarcane is an economically significant agricultural crop grown in tropical and subtropical areas for the production of sugar, ethanol, biofuel, and related by-products. The extraction of viable buds for planting is crucial to its propagation. Conventional bud cutting requires considerable manual effort, is labor-intensive and is prone to inconsistencies. This research presents a Vision-Based Sugarcane Bud Detection and Cutting Unit (VSBD-CU) designed to automate the processes of bud detection and cutting through the integration of computer vision and mechatronics. The system employs YOLOv8, for bud localization, along with a Raspberry Pi-4B managed electromechanical cutting mechanism. The system demonstrates high performance with mature buds, achieving a mean Average Precision (mAP@0.5) of 98.1 %. Real-time detection ranges from 0.87 to 0.95 s, with a cutting throughput of up to 1696 buds per hour. Detection efficiency for immature buds was observed to be lower, averaging 25.56 %, attributed to obstructions such as the leaf sheath. Comprehensive experimental assessments were performed to examine the influence of blade bevel angles (30°, 45°, and 60°) and sugarcane varieties (CoX 20055, CoP 18437, and CoX 20246) on cutting rate and duration. ANOVA statistical analysis indicated that blade bevel angle has a significant effect on cutting rate (F = 2268.49) and cutting time (F = 5555.71), with significant interaction effects observed between blade angle and cane variety (p < 0.001). A blade angle of 30° consistently provided optimal performance across all varieties. The proposed system demonstrates significant potential for improving precision agriculture via future robotic integration and field implementation.