To overcome the inefficiency, high expense, and subjective inconsistency of manual grading, we introduce a lightweight oyster shape recognition approach built upon an enhanced YOLOv10 framework. . First,the backbone of YOLOv10n is replaced with PP -LCNet; second,a Focused Linear Attention module is inserted into the PSA blocks of the backbone; third,the upsampling operator in the neck is substituted with the DySample dynamic upsampler; ultimately, the original activation is superseded by the AReLU function. Compared with the original YOLOv10n,the enhanced model trims floating- point operations by 20. 7% ,parameters by 22. 2% ,and overall size by 22. 4% ,while raising precision to 94. 4%—a 3. 8-point improvement. The proposed approach not only provides an effective solution for oyster shape recognition but also offers technical support for developing automatic identification and classification systems,thereby advancing the intelligent development of oyster sorting technology.