English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Biodiversity monitoring is crucial for tracking and counteracting adversetrends in population fluctuations. However, automatic recognition systems arerarely applied so far, and experts evaluate the generated data masses manually.Especially the support of deep learning methods for visual monitoring is notyet established in biodiversity research, compared to other areas likeadvertising or entertainment. In this paper, we present a deep learningpipeline for analyzing images captured by a moth scanner, an automated visualmonitoring system of moth species developed within the AMMOD project. We firstlocalize individuals with a moth detector and afterward determine the speciesof detected insects with a classifier. Our detector achieves up to 99.01% meanaverage precision and our classifier distinguishes 200 moth species with anaccuracy of 93.13% on image cutouts depicting single insects. Combining both inour pipeline improves the accuracy for species identification in images of themoth scanner from 79.62% to 88.05%.

Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification