Utku, Anıl; Ayaz, Zafer; Ciftci, Derya; Ali Akcayol, M.

Syrphidae 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.

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