This study aimed to compare the effectiveness of the YOLOv8n algorithm with different deep learning models in the task of automatic detection and counting of Colossoma macropomum postlarvae, a key process to improve aquaculture production and reduce mortality in this field. In order to evaluate the quality of the results, metrics such as accuracy, sensitivity, F1-Score and processing time were analyzed, comparing the performance of YOLOv8n with PP-PicoDet-det-L, Faster R-CNN and Grid R-CNN. The methodology employed included preprocessing and data augmentation techniques applied to a set of 71 images obtained from various mobile devices, which ensured greater representativeness and quality of the sample. The training of the algorithms was carried out in 12 epochs, using both a supercomputer and a workstation provided by IIAP. The results indicate that YOLOv8n exhibits superior performance...