Aquatic invasive species, such as red swamp crayfish in Japan and Europe, have significant impacts on biodiversity, human health and the economy. Quantifying the distribution of invasive species is a critical step in preventing and reducing their invasion, however, effective methods have not yet been established. Here, we aim to provide a low-cost framework for monitoring aquatic invasive species combining underwater imaging and machine learning based detection.