Natural seedlings are collected for use at Pacific oyster (Magallana gigas) aquaculture sites in Japan. For seedling collection to be successful, aquaculture farmers identify target larvae through microscopic observation, using morphological features such as the shell height/shell length ratio, lateral asymmetry of the shell, and umbo development. To reduce the time and labor associated with larval identification, we developed a deep-learning-based Pacific oyster larvae identification system, which consisted of a tablet connecting a new photographic device to capture images for identification. The final accuracies of the trained identification system for the learning datasets for measuring accuracy were 82.5% and 80.5% for precision (P) and recall (R), respectively, for all sizes of larvae (shell height ≥100µm). We verified our identification system at an oyster aquaculture site. The obtained accuracy in the verification was low for some samples (63.6%–100.0% and 41.9%–100.0% for P and R, respectively, for pre-attachment stage larvae), indicating a decrease in identification accuracy for the in-situ samples. The decrease likely occurred owing to the natural variability of larvae. We modified the learning dataset with high variation in oyster larval images and retrained the identification system. The accuracy of the retrained identification system was increased to 83.9%–100.0% for P and R and thus satisfied the requirements for practical application for pre-attachment stage larvae. For small-sized larvae, P was improved but R was relatively low, indicating that although the detection level of our identification model for oyster larvae is low, the accuracy of the detected oyster larvae is high.