Harmful Algae Image Classification With Machine Learning

Abstract

"Harmful algae can grow rapidly into blooms and release toxins that can contaminate drinking water sources which can be unsafe to humans and wildlife. This research has discovered images belonging to seven different genera of harmful algae which were used as a data set to train a convolutional neural network (CNN) to automatically classify harmful algae at the microscopic level. After data augmentation, 20,010 images were trained on the network. Our CNN has shown an overall accuracy of 99.75%. The next steps include collecting more original images and modification the neural network architecture to improve the accuracy of unseen images."