Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae) DOI Creative Commons
Mohammad Fraiwan

Animals, Journal Year: 2024, Volume and Issue: 14(24), P. 3712 - 3712

Published: Dec. 23, 2024

Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn dusk, female sandflies feed on blood to facilitate egg production. In doing so, they can transmit infectious diseases that may cause symptoms such as fever, headaches, muscle pain, anemia, skin rashes, ulcers. Importantly, species-specific their disease transmission. Determining gender species of typically involves examining morphology internal anatomy using established identification keys. However, this process requires expert knowledge is labor-intensive, time-consuming, prone misidentification. paper, we develop a highly accurate efficient convolutional network model utilizes pharyngeal genital images sandfly samples classify sex three (i.e., Phlebotomus sergenti, Ph. alexandri, papatasi). A detailed evaluation model’s structure classification performance was conducted multiple metrics. The results demonstrate an excellent sex-species accuracy exceeding 95%. Hence, it possible automated artificial intelligence-based systems serve entomology community at large specialized professionals.

Language: Английский

Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives DOI
Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia‐Garcia

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109412 - 109412

Published: Sept. 7, 2024

Language: Английский

Citations

19
Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure DOI
Chuanyong Wang, Jie Shen

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 376, P. 124502 - 124502

Published: Feb. 15, 2025

Language: Английский

Citations

0
Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species DOI Creative Commons
Mohammad Fraiwan, Rami Mukbel,

Dania Kanaan

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0320224 - e0320224

Published: April 3, 2025

Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible disease transmission. Thus, effective classification of species the corresponding sex identification important surveillance control, managing breeding/populations, research development, conducting epidemiological studies. This is typically performed manually by observing internal morphological features, which maybe an error-prone tedious process. In this work, we developed a deep learning artificial intelligence system to determine gender differentiate between three two subgenera (i.e., Phlebotomus alexandri , papatasi sergenti ). Using locally field-caught prepared samples over period years, based on convolutional neural networks, transfer learning, early fusion genital pharynx images, achieved exceptional accuracy (greater than 95%) across multiple performance metrics using wide range pre-trained network models. study not only contributes field medical entomology providing automated accurate solution taxonomy, but also establishes framework leveraging techniques similar vector-borne control efforts.

Language: Английский

Citations

0
Future of Information Systems for Pest Management: Data Acquisition and Integration to Guiding Management Decisions DOI
Mahendra Bhandari, Pankaj Pal, Michael J. Brewer

et al.

CABI eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 251 - 262

Published: Aug. 22, 2024

Language: Английский

Citations

1
Future of Information Systems for Pest Management: Data Acquisition and Integration to Guiding Management Decisions DOI
Mahendra Bhandari, Pankaj Pal, Michael J. Brewer

et al.

CABI eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 251 - 262

Published: Aug. 23, 2024

Language: Английский

Citations

1
From ethology to behavioral biology DOI
Michael Taborsky

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

1
Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae) DOI Creative Commons
Mohammad Fraiwan

Animals, Journal Year: 2024, Volume and Issue: 14(24), P. 3712 - 3712

Published: Dec. 23, 2024

Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn dusk, female sandflies feed on blood to facilitate egg production. In doing so, they can transmit infectious diseases that may cause symptoms such as fever, headaches, muscle pain, anemia, skin rashes, ulcers. Importantly, species-specific their disease transmission. Determining gender species of typically involves examining morphology internal anatomy using established identification keys. However, this process requires expert knowledge is labor-intensive, time-consuming, prone misidentification. paper, we develop a highly accurate efficient convolutional network model utilizes pharyngeal genital images sandfly samples classify sex three (i.e., Phlebotomus sergenti, Ph. alexandri, papatasi). A detailed evaluation model’s structure classification performance was conducted multiple metrics. The results demonstrate an excellent sex-species accuracy exceeding 95%. Hence, it possible automated artificial intelligence-based systems serve entomology community at large specialized professionals.

Language: Английский

Citations

1