There is a substantial requirement for the modernisation of our agricultural practices. Current methods can be susceptible to labour shortages, damaging to the environment, and inefficient; and these factors can lead to food wastage so threatening food security. This paper aims to address this issue by employing 2D/3D computer vision and machine learning to assist with robotic harvesting of shiitake mushrooms. Shiitake mushrooms are one of the most valuable gourmet mushrooms, but also the most labour intensive to grow. The work described demonstrates that state-of-the-art machine vision and deep learning techniques are useful for this application; and are effective at multiple stages throughout the harvest of shiitake mushrooms. Following the creation of the first publicly available segmented shiitake mushroom dataset, YOLOv8-seg and Detectron2 Mask R-CNN models were trained to an Average Precision of 94.9% and 77.7% respectively, segmenting up to 85 shiitake mushrooms in a single image. Additional exploratory work with a smaller keypoint dataset was conducted to determine the suitability of both of these architectures to plot the cut points for the harvest operation, with results proving the feasibility for the task.