The Hatching-Box: A Novel System for Automated Monitoring and Quantification of Drosophila melanogaster Developmental Behavior
Julian Bigge, Maite Ogueta, Luis Garcia, Benjamin Risse
TL;DR
The paper presents the Hatching-Box, a scalable, open-source system that automates long-term monitoring of Drosophila development directly within standard rearing vials. It combines a purpose-built hardware setup with a YOLOv7-based detection pipeline and an identity-preserving tracker to quantify larvae, pupae, and adults across days, outputting trajectories in HDF5 for downstream analysis. Validation across a curated 470k-object dataset and circadian experiments reproduces known clock phenotypes and demonstrates the system’s ability to reconstruct full life-cycles and group behaviors with minimal manual intervention. The approach offers high-throughput, low-labor automated monitoring suitable for diverse developmental and circadian studies, with potential for integration into broader cultivation workflows.
Abstract
In this paper we propose the Hatching-Box, a novel imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila in standard rearing vials and during regular rearing routines, rendering explicit experiments obsolete. This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled/empty pupae and flies over multiple days. Given the affordable and reproducible design of the Hatching-Box in combination with our generic client/server-based software, the system can easily be scaled to monitor an arbitrary amount of rearing vials simultaneously. We evaluated our system on a curated image dataset comprising nearly 470,000 annotated objects and performed several studies on real world experiments. We successfully reproduced results from well-established circadian experiments by comparing the eclosion periods of wild type flies to the clock mutants $\textit{per}^{short}$, $\textit{per}^{long}$ and $\textit{per}^0$ without involvement of any manual labor. Furthermore we show, that the Hatching-Box is able to extract additional information about group behavior as well as to reconstruct the whole life-cycle of the individual specimens. These results not only demonstrate the applicability of our system for long-term experiments but also indicate its benefits for automated monitoring in the general cultivation process.