The Challenges of Detecting Eurasian Watermilfoil with a Pseudo Labeling Semi-Supervised Convolutional Neural Network

Abstract

Eurasian Watermilfoil is an invasive aquatic plant found in many bodies of water in Minnesota. It tends to out grow and kill many native plants. The current solution to removing Eurasian Watermilfoil is to kill it using a herbicide. However, this has drawbacks because the herbicide can affect native plants, it contaminates the water, and is not sprayed accurately. A solution to this problem is by using autonomous underwater vehicles equipped with a deep learning model that can detect Eurasian Watermilfoil to map it for accurate spraying. However we found this not to be the case. While trying to train a model to detect Eurasian Watermilfoil using a pseudo labeling semi-supervised and supervised convolutional neural network, it could not detect the plant due to the scarce amount of images. However it was found the pseudo labeling a diver dataset proved to be more accurate and efficent than the supervised version.