Open AccessSPECIES IDENTIFICATION FOR AQUATIC BIOMONITORING USING DEEP RESIDUAL CNN AND TRANSFER LEARNING
Author(s) -
Aleksandar Milosavljević,
Đurađ Milošević,
Bratislav Predić
Publication year - 2021
Publication title -
facta universitatis. series: automatic control and robotics
Language(s) - English
Resource type - Journals
eISSN - 1820-6425
pISSN - 1820-6417
DOI - 10.22190/fuacr201118001m
Subject(s) - transfer of learning , chironomidae , computer science , convolutional neural network , artificial intelligence , biomonitoring , robustness (evolution) , benthic zone , machine learning , deep learning , aquatic ecosystem , identification (biology) , environmental science , ecology , biology , biochemistry , larva , gene
Aquatic insects and other benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, an expensive and time-consuming process of species identification represents one of the key obstacles for reliable biomonitoring of aquatic ecosystems. In this paper, we proposed a deep learning (DL) based method for species identification that we evaluated on several available public datasets (FIN-Benthic, STONEFLY9, and EPT29) along with our Chironomidae dataset (CHIRO10). The proposed method relies on three DL techniques used to improve robustness when training is done on a relatively small dataset: transfer learning, data augmentation, and feature dropout. We applied transfer learning by employing ResNet-50 deep convolutional neural network (CNN) pretrained on ImageNet 2012 dataset. The results show significant improvement compared to original contributions and confirms that there is a considerable gain when there are multiple images per specimen.