WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 23, 2026
Virtual Laboratory for Identifying Fruit Flies using Deep Learning and Augmented Reality on Mobile Devices
Authors: , ,
Search Articles
Abstract: Agronomy students need to identify agricultural pests like fruit flies of the genus Anastrepha, a task often hindered by the lack of interactive tools. This study develops a mobile "virtual laboratory" that integrates augmented reality (AR) with deep learning for species recognition in biointensive fruit-growing ecosystems. The MobileNetV2 architecture was chosen over VGG16 for its greater efficiency-to-accuracy ratio. The Barracuda inference worked with the Unity engine so that it could handle data in real time. A test set of 200 images was used for experimentation, yielding an F1 score of 0.98 and an accuracy of 98%. Usability tests were also conducted to measure the optimal AR scanning distance, which showed that the ideal distance was 15cm. Therefore, it can be concluded that this is an effective tool for species identification, with low latency and no internet connection required for cloud services.
Pages: 381-391
DOI: 10.37394/23209.2026.23.31