Advances in machine learning for visual recognition and ultra-low surface brightness imaging have made it possible to detect older and fainter historic supernova light echoes (SN LEs). We are particularly interested in the historic core-collapse SN (CCSN) Crab (SN 1054), as it is the only CCSN with records of direct-light observations in the last 1000 years. We have improved the SN LE machine-learning Python package ALED (Automated Light Echo Detection), created by Bhullar et al. 2021, by adding false positive masks as an additional input. ALED is visual recognition software that identifies and locates LEs in difference images. Before the invention of ALED, LE images had to be categorized by visual inspection, which was a very time-consuming task. Additionally, we have developed a method for manufacturing and augmenting LE training sets, which has previously not been applied to LEs. We manufactured Dragonfly Telephoto Array (DTA) LEs by extracting LEs from Canada-France-Hawaii Telescope difference images and overlaying them on DTA difference images. The DTA is a promising tool for LE detection because of its ability to observe ultra-low surface brightness structures. Additionally, we augmented the only existing DTA LE image by overlaying it on other DTA images. Both of these procedures provided options for further augmentation, such as changing the LE's brightness and width. We also created a process to mask the bright star difference artifacts in DTA images. These stars are typically mislabeled as LEs, and hence masking them makes LE identification simpler. We have created an effective DTA training set for ALED, which is prepared to search for LEs around the historic CCSN Crab (SN 1054), once more DTA images in that region are procured. / Thesis / Master of Science (MSc)