The week's pick
Random Articles
Reseach Article
CNN-based Recognition of Sandfly Morphology for Vector Identification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 69 |
| Year of Publication: 2025 |
| Authors: Akarid Abderrahim, El Adib Samir, Ait El Asri Smail, Raissouni Naoufal |
10.5120/ijca2025926170
|
Akarid Abderrahim, El Adib Samir, Ait El Asri Smail, Raissouni Naoufal . CNN-based Recognition of Sandfly Morphology for Vector Identification. International Journal of Computer Applications. 187, 69 ( Dec 2025), 58-61. DOI=10.5120/ijca2025926170
Abstract
Insects are one of the beautiful creations of god and they exist in millions of different species and colors. Identifying each of them requires a biologist and entomologist with immense knowledge and skills. In this rising era of technologies most of the impossible are made possible by incorporating artificial intelligence into real world problems. By introducing machine learning algorithms such as Convolutional neural networks for identifying Insects species with just an image would be a great help for agriculture, public health, and biological research. The sandfly species recognizes that are vectors of leishmaniasis in a specific geographical area. This paper tries to introduce convolutional neural networks to efficiently identify sandflies Based on morphological and taxonomic characters: head (Cibarium + Pharynx), both male and female genitalia, wings, and body just feeding an image of the sandfly to be recognized. In this system taking an image in your mobile camera, uploading it and just clicking the predict button is all that is needed to know more about morphological and taxonomic characters of the insect that you have just seen.
References
- C. A. Triplehorn and N. F. Jonnson, Estudo dos Insetos. 2011.
- R. G. Foottit and P. H. Adler, Insect Biodiversity: Science and Society. 2009. doi: 10.1002/9781444308211.
- F. Dantas-Torres, V. D. Tarallo, and D. Otranto, “Morphological keys for the identification of Italian phlebotomine sand flies (Diptera: Psychodidae: Phlebotominae),” Parasites and Vectors, 2014, doi: 10.1186/s13071-014-0479-5.
- M. Xin and Y. Wang, “Image Recognition of Crop Diseases and Insect Pests Based on Deep Learning,” Wirel. Commun. Mob. Comput., 2021, doi: 10.1155/2021/5511676.
- M. Fraiwan, R. Mukbel, and D. Kanaan, “Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species,” PLoS One, vol. 20, no. 4 April, pp. 1–16, 2025, doi: 10.1371/journal.pone.0320224.
- S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, 2015.
- H. Xu, Y. Huang, and M. Liu, “Research on pest detection and identification of corn leaf based on improved YOLOv3 model,” J. Nanjing Agric. Univ., 2022, doi: 10.7685/jnau.202110039.
- M. F. Shahzad, S. Xu, W. M. Lim, X. Yang, and Q. R. Khan, “Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning,” Heliyon, vol. 10, no. 8, Apr. 2024, doi: 10.1016/j.heliyon.2024.e29523.
- M. Valan, K. Makonyi, A. Maki, D. Vondráček, and F. Ronquist, “Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks,” Syst. Biol., 2019, doi: 10.1093/sysbio/syz014.
- M. Fraiwan, R. Mukbel, and D. Kanaan, “A dataset of sandfly (Phlebotomus papatasi, Phlebotomus alexandri, and Phlebotomus sergenti) genital and pharyngeal images,” Data Br., vol. 57, p. 111031, 2024, doi: 10.1016/j.dib.2024.111031.
Index Terms
Keywords