Visual Identification of Oysters Using Machine Learning

Agricultural technology has evolved significantly since the 1800s. However, with respect to monitoring and farming of aquaculture such as oysters, industry is still stuck in the past. This project aims to modernize oyster monitoring and identification with machine learning. We developed a program capable of accurately identifying and classifying oysters in three states. They include closed, meaning they are healthy, or open for long periods of time, indicating mortality, and indeterminate. We also increased the scope of existing software with a website for real-time identification that is accessible to the public. In the process, we added support for video annotation, which was previously difficult to perform for those with no prior coding experience.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>