Author(s)

Jiajie Qiu ¹, Xiaoying Su ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Xiaoying Su

ABSTRACT

To address the practical need for rapid ripeness detection of fruits after harvesting, an apple ripeness grading system based on a lightweight ResNet18 model is designed. Taking Red Fuji apples as the research object, images of apples at different ripening stages are collected by image acquisition equipment. After expanding the dataset through data augmentation, the ResNet18 model is optimized by channel pruning and activation function improvement to construct a lightweight classification model suitable for terminal deployment. Experimental results show that the improved model achieves an accuracy of 94.2% on the test set, with a 42% reduction in model parameter scale and a 35% increase in inference speed, which can meet the practical application requirements of rapid apple ripeness grading.

KEYWORDS

Machine Vision; ResNet18; Lightweight Improvement; Apple Ripeness; Image Classification

CITE THIS PAPER

Jiajie Qiu, Xiaoying Su, Design and Implementation of Apple Ripeness Grading System Based on Lightweight ResNet18. Journal of Electronics and Information Science (2025) Vol. 10: 149-153. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100218.

REFERENCES

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