Automated Eight-Stage Classification of Drosophila melanogaster Using Transfer-Learning CNNs with Mobile Live-Inference Deployment

Abstract

Drosophila melanogaster is a foundational model organism whose rapid development and genetic tractability underpin research in genetics, development, and disease. Manual staging of its embryonic, larval, and pupal phases is slow and error-prone. An automated eight-class classifier is introduced to distinguish egg, first-, second-, and third-instar larvae, as well as white, brown, eye, and black pupae, from stereo-microscope images. By fine-tuning ImageNet-pretrained CNNs (ResNet-50, InceptionV3, ConvNeXtSmall) on a balanced dataset ([~]300 images per class), the best-performing model (ResNet-50) achieves 85% accuracy (F1 = 0.85) on a held-out validation set, significantly outperformed alternatives. Primary misclassifications align with subtle morphological transitions between adjacent stages. To facilitate broad adoption, the ResNet-50 model has been deployed in a lightweight Streamlit app offering live-camera inference ({approx}8 FPS on mobile). All code, pretrained weights, and data are publicly available, enabling scalable, high-throughput Drosophila staging for diverse experimental workflows.