Research Article
DeepSquitoes: A Mobile System Framework for the Surveillance of Disease-Carrying Mosquitoes
@INPROCEEDINGS{10.1007/978-3-031-60665-6_27, author={Sudha Cheerkoot-Jalim and Camille Simon-Chane and Zarine Cadersaib and Leckraj Nagowah and Zahra Mungloo-Dilmohamud and Denis Sereno and Kavi Kumar Khedo and Shakuntala Baichoo and Soulakshmee D. Nagowah and Abha Jodheea-Jutton and Fadil Chady and Aymeric Histace}, title={DeepSquitoes: A Mobile System Framework for the Surveillance of Disease-Carrying Mosquitoes}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Entomological Surveillance System Deep Learning Image Classification}, doi={10.1007/978-3-031-60665-6_27} }- Sudha Cheerkoot-Jalim
Camille Simon-Chane
Zarine Cadersaib
Leckraj Nagowah
Zahra Mungloo-Dilmohamud
Denis Sereno
Kavi Kumar Khedo
Shakuntala Baichoo
Soulakshmee D. Nagowah
Abha Jodheea-Jutton
Fadil Chady
Aymeric Histace
Year: 2024
DeepSquitoes: A Mobile System Framework for the Surveillance of Disease-Carrying Mosquitoes
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_27
Abstract
Insects that spread diseases like malaria, chikungunya and Lyme disease are found all over the world because of climate change, economic fluctuations, human migration, and international trade. In this study, we proposeDeepSquitoes, a mobile system framework for insect identification and fast data dissemination, with the goal of improving the management of public health hazards.DeepSquitoesspecialises in the quick identification of mosquitoes, which are common in tropical areas, and can be used to monitor insect population movements in real-time. To maximise user interaction and data accuracy, the application includes geolocation-based identification, sophisticated preprocessing, and specialised annotation. Image preprocessing techniques like Gaussian Blur and contour extraction are applied on mosquito wing images to ensure data quality. Deep learning algorithms are trained on the preprocessed images for mosquito species classification. The image recognition model performs well, with a 93% training accuracy and a 74% validation accuracy using MobileNetV2 from TensorFlow. Our local dataset, which included 154 images of eight different insect species, had a commendable recognition accuracy rate of 76%.
