Neural network model approach for automated benthic animal identification

Abstract

The most tedious and hectic job is to identify the tiny benthic animals by spending thousands of hour under the microscope, since all the fauna need to be counted, sorted, picked and permanently mounted on glass slides for taxonomic identification. All faunal identifications need a lot of preprocessing and it consumes a lot of time to identify a single specimen. Therefore, to reduce the complexity of many such procedures, combined with the desire to identify larger datasets, we came up with new software based on artificial intelligence which can automatically identify the benthic fauna through the microscopic images. In this paper, we propose a machine learning method for automatic visual identification through the images of the benthic fauna. To this end, we propose a neural network model, where we demonstrate that the proposed approach differentiates the fauna based on images. However, it works well with vast amounts of image data and significant computational resources.