Imagelytics: A Deep Learning-Based Image Classification Tool to Support Bioassessment

Bioassessment is the process of using living organisms to assess the ecological health of a particular ecosystem. It typically relies on identifying specific organisms that are sensitive to changes in environmental conditions. Benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, a time-consuming process of species identification that requires high expertise represents one of the key obstacles to reliable bioassessment of aquatic ecosystems. Partial automation of this process using deep learning-based image classification is the goal of an ongoing project AIAQUAMI we are participating in. One of the project deliverables is a standalone desktop application for image classification with visualization and reporting that we developed and open-sourced as Imagelytics. The application relies on a convolutional neural network (CNN) to classify images and the Grad-CAM algorithm to produce heatmaps of the image areas that mostly influenced the network decision. Along with the application code, we also open-sourced scripts that can be used to train CNN on an arbitrary dataset and produce required metadata, so it can be used with Imagelytics. In this paper, we presented technical details about the application and training method that will enable its general use for image classification tasks. As a part of the evaluation, we will show a use case related to species identification of non-biting midges (Diptera: Chironomidae).