DaphnAI: A Deep Learning Approach for High-Throughput Zooplankton Community Analysis

Abstract

Zooplankton are critical components of freshwater ecosystems, mediating energy transfer and regulating trophic dynamics. Monitoring their community composition and population structure is essential for understanding ecological responses to environmental change. However, conventional approaches rely on manual processing and classification, which are time-consuming, prone to error, and unsuitable for high-throughput applications. Here, we present a deep learning framework based on the YOLOv12n-seg architecture for the automated identification, instance segmentation, and quantification of zooplankton from high-resolution images. Trained on mesocosm data containing multiple species zooplankton taxa, our model achieved a mean average precision (mAP@50) of 0.899 and demonstrated a 230-fold speed increase over manual annotation, even on standard non-GPU hardware. Unlike previous approaches that focus solely on classification, our model produces pixel-precise segmentation masks, enabling accurate estimation of individual size metrics. This expands the scope of automated zooplankton monitoring from presence/absence data to demographic assessments, including shifts in size distributions and the possibility to be used for unraveling morphological adaptations. Our work demonstrates how deep learning can overcome longstanding limitations in ecological monitoring by enabling scalable, reproducible, and high-resolution quantification of community composition. This approach opens the door to large-scale, long-term studies of freshwater ecosystems.