Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via mechanical weeders. Automation can support this difficult work for weed management via driveway detection. In this context, deep learning algorithms have the potential to train models to detect driveways through furrow image segmentation. Therefore, the purpose of this research was to develop an image segmentation model for automated weed control operations in cassava plantation fields. To achieve this, image datasets were obtained from various fields to aid weed detection models in automated weed management. Three models—Mask R-CNN, YOLACT, and YOLOv8n-seg—were used to construct the image segmentation model, and they were evaluated according to their precision, recall, and FPS. The results show that YOLOv8n-seg achieved the highest accuracy and FPS (114.94 FPS); however, it experienced issues with frame segmentation during video testing. In contrast, YOLACT had no segmentation issues in the video tests (23.45 FPS), indicating its potential for driveway segmentation in cassava plantations. In summary, image segmentation for detecting driveways can improve weed management in cassava fields, and the further automation of low-cost mechanical weeders in tropical climates can be performed based on the YOLACT algorithm.