2026-10-29T21:48:07Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/24702026-10-28T15:29:47Zopenairedrivercom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Deep Learning Based Classification for Hoverflies (Diptera: Syrphidae)Utku, AnılAyaz, ZaferCiftci, DeryaAli Akcayol, M.automatic species identificationConvolutional neural networkimage classificationSyrphidae is essential in pollinating many flowering plants and cereals and is a family with high species diversity in the order Diptera. These family species are also used in biodiversity and conservation studies. This study proposes an image-based CNN model for easy, fast, and accurate identification of Syrphidae species. Seven hundred twenty-seven hoverfly images were used to train and test the developed deep-learning model. Four hundred seventy-nine of these images were allocated to the training set and two hundred forty-eight to the test dataset. There are a total of 15 species in the dataset. With the CNN-based deep learning model developed in this study, accuracy 0.96, precision 0.97, recall 0.96, and f-measure 0.96 values were obtained for the dataset. The experimental results showed that the proposed CNN-based deep learning model had a high success rate in distinguishing the Syrphidae species.Gazi Entomological Research Society2023Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/2470https://doi.org/10.51963/jers.v25i3.24451302-0250enJournal of the Entomological Research Societyinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/2470