Deep Learning Based Classification for Hoverflies (Diptera: Syrphidae)
JOURNAL OF THE ENTOMOLOGICAL RESEARCH SOCIETY, cilt.25, sa.2, ss.529-544, 2023 (SCI-Expanded, Scopus, TRDizin)
- 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.