Deep Learning Based Classification for Hoverflies (Diptera: Syrphidae)

Ayaz Z., Akcayol M. A., Çiftçi D., Utku A.

JOURNAL OF THE ENTOMOLOGICAL RESEARCH SOCIETY, cilt.25, sa.2, ss.529-544, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 2
  • Basım Tarihi: 2023
  • Doi Numarası: 10.51963/jers.v25i3.2445
  • Dergi Adı: JOURNAL OF THE ENTOMOLOGICAL RESEARCH SOCIETY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.529-544
  • Gazi Üniversitesi Adresli: Evet

Özet

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.