Judgment of water transparency in aquariums by deep learning using camera images

Abstract

In order for the water purification system to operate efficiently in aquariums according to the real-time water conditions, it is necessary to automatically judge the transparency of water. We trained a convolutional neural network (CNN) model to judge the water transparency from camera images of an aquarium. The images of the aquarium are classified into three categories with respect to the water transparency: clear, little turbid, and turbid. The CNN model was trained to judge the water transparency from the grayscale images converted from the original RGB images. Cross validation within the collected dataset of 48 days was performed to evaluate the CNN's performance, and it was shown that, although the accuracy for the images at transition periods of transparency was slightly low, the trained CNN was able to judge the water transparency with high accuracy.