Résumé
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 propose DeepSquitoes, a mobile system framework for insect identification and fast data dissemination, with the goal of improving the management of public health hazards. DeepSquitoes specialises 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%.