Researchers from the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) in Oberpfaffenhofen, the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, the German Centre for Integrative Biodiversity Research (iDiv) in Leipzig, the Martin Luther University Halle-Wittenberg and our Earth Observation Research Cluster (EORC) of our Julius-Maximilians-University in Würzburg collaborated on a study exploring how uncertainty quantification can improve the classification of European pollinating flies. The paper titled “Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators” was just published in PLOS One by Thomas Stark, Michael Wurm, Valentin Ștefan, Felicitas Wolf, Hannes Taubenböck & Tiffany M. Knight.
Here is the abstract of the paper:
Pollination is essential for maintaining biodiversity and ensuring food security, and in Europe it is primarily mediated by four insect orders (Coleoptera, Diptera, Hymenoptera, Lepidoptera). However, traditional monitoring methods are costly and time consuming. Although recent automation efforts have focused on butterflies and bees, flies, a diverse and ecologically important group of pollinators, have received comparatively little attention, likely due to the challenges posed by their subtle morphological differences. In this study, we investigate the application of Convolutional Neural Networks (CNNs) for classifying 15 European pollinating fly families and quantifying the associated classification uncertainty. In curating our dataset, we ensured that the images of Diptera captured diverse visual characteristics relevant for classification, including wing morphology and general body habitus. We evaluated the performance of three CNNs, ResNet18, MobileNetV3, and EfficientNetB4 and estimated the prediction confidence using Monte Carlo methods, combining test-time augmentation and dropout to approximate both aleatoric and epistemic uncertainty. We demonstrate the effectiveness of these models in accurately distinguishing fly families. We achieved an overall accuracy of up to 95.61%, with a mean relative increase in accuracy of 5.58% when comparing uncropped to cropped images. Furthermore, cropping images to the Diptera bounding boxes not only improved classification performance across all models but also increased mean prediction confidence by 8.56%, effectively reducing misclassifications among families. This approach represents a significant advance in automated pollinator monitoring and has promising implications for both scientific research and practical applications.
Here is the link to the full paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0323984&?utm_id=plos111
This paper is related to a previous studies on the topic:
· https://www.nature.com/articles/s41598-023-43482-3
