The accurate quantification and continuous monitoring of Brachionus plicatilis. rotifer cultures are essential for aquaculture and aquatic animal research laboratories. Manual counting methods are labor-intensive, error-prone, and inefficient for large-scale operations, necessitating automated solutions. This study presents the Rotiferometer, an automated and cost-effective system that integrates mechanical design, deep learning, and automation for precise rotifer detection, classification and counting. Using a YOLOv8 model, the system achieves a mean average precision (mAP@0.5) of 94.7 % in distinguishing gravid and non-gravid rotifers. It proceeds by scanning a 1 mL Sedgewick Rafter slide under 3 min, ensuring rapid and accurate enumeration. A strong correlation was observed between manual and Rotiferometer counts, (with R2 values of 0.9729 and 0.9868 for gravid (egg-bearing) and non-gravid (non-egg-bearing) rotifers, respectively), confirming the system’s accuracy. Additionally, the analysis of operator variability using the Rotiferometer delivered consistent results regardless of the user, minimizing the need for specialized expertise. Finally, a 45-day monitoring experiment with the Rotiferometer effectively tracked rotifer population changes, identifying key phases of growth, decline, and recovery. These results highlight the device’s potential to enhance rotifer culture management by providing real-time, reliable, and automated monitoring, thereby optimizing aquaculture productivity and research efficiency.