The Effects of Data Quality on Deep Learning Performance for Aquatic Insect Identification: Advances for Biomonitoring Studies

Abstract

Deep learning models, known as convolutional neural networks (CNNs), have paved the way for reliable automated image recognition. These models are increasingly being applied in research on freshwater biodiversity, aiming to enhance efficiency and taxonomic resolution in biomonitoring. However, insufficient or imbalanced datasets remain a significant bottleneck for creating high-precision classifiers. The highly imbalanced data, where some species are rare and others are common, are typical of the composition of most benthic communities. In this study, a series of CNN models was built using 33 species of aquatic insects, with datasets ranging from 10 to 80 individuals, to determine the optimal number of individuals each class should have to build a high-precision classifier. We also consider the effect of class imbalance in the training dataset and the use of oversampling technique. The results showed that a robust model with acceptable accuracy (99.45%) was achieved with at least 30 individuals per class. A strongly imbalanced dataset caused an approximately 2% decrease in classification accuracy, while a moderately imbalanced dataset had no significant effect. The application of the oversampling technique enhanced in 1.88% the accuracy of strongly imbalanced models. These findings can help effectively tailor future aquatic macroinvertebrate training datasets.