  Process refinement to consistently produce high-quality material over a large
area of the grown crystal, enabling various applications from optics crystals
to quantum detectors, has long been a goal for diamond growth. Machine learning
offers a promising path toward this goal, but faces challenges such as the
complexity of features within datasets, their time-dependency, and the volume
of data produced per growth run. Accurate spatial feature extraction from image
to image for real-time monitoring of diamond growth is crucial yet complicated
due to the low-volume and high feature complexity nature of the datasets. This
paper compares various traditional and machine learning-driven approaches for
feature extraction in the diamond growth domain, proposing a novel deep
learning-driven semantic segmentation approach to isolate and classify accurate
pixel masks of geometric features like diamond, pocket holder, and background,
along with their derivative features based on shape and size. Using an
annotation-focused human-in-the-loop software architecture for training
datasets, with modules for selective data labeling using active learning, data
augmentations, and model-assisted labeling, our approach achieves effective
annotation accuracy and drastically reduces labeling time and cost. Deep
learning algorithms prove highly efficient in accurately learning complex
representations from datasets with many features. Our top-performing model,
based on the DeeplabV3plus architecture, achieves outstanding accuracy in
classifying features of interest, with accuracies of 96.31% for pocket holder,
98.60% for diamond top, and 91.64% for diamond side features.
