Quality recognition method of oyster based on U-net and random forest

Oysters are one of the most important cultivated marine resources globally. The shape of oysters is an essential reference criterion for consumers to judge the quality of oysters. In order to recognize oyster's shape, the U-Net model and random forest are combined to compose a creative strategy. To be more specific, the U-Net neural network model is firstly developed to segment the image and obtain the contours of oysters, and the shape features of oysters are extracted. Then, a random forest model with shape feature parameters depending on customer preference is created to identify oyster quality. The results indicate that the intersection-over-union of segmentation outcomes achieved by U-Net reaches 99.06%, surpassing the 93.50% obtained by traditional methods. The accuracy of the classification strategy based on the shape features parameters of consumer preference is 94.18%, which further proves the effectiveness of the proposed strategy. This study might provide valuable data and guidelines to oyster product classification based on shell shape within market contexts.

Automated summary

This study proposes a creative strategy to recognize the shape of oysters and classify their quality based on customer preference. By combining the U-Net model and random forest, the researchers achieved high accuracy in segmentation and classification, providing valuable data and guidelines for oyster product classification within market contexts.