Fruit Freshness Detection Based on YOLOv8 and SE attention Mechanism

Authors

  • Zihe Wei
  • Miaosen Chang
  • Yipeng Zhong

DOI:

https://doi.org/10.54097/ajst.v6i1.9125

Keywords:

Fruit freshness detection, YOLOv8, SE attention mechanism.

Abstract

Fruit is a crucial component of daily diets, emphasizing the importance of ensuring its freshness. Our proposed method utilizes YOLOv8 and SE attention mechanism for detecting the freshness of fruits. Our method balances accuracy and real-time performance while detecting the freshness of different types of fruits. The proposed method achieves an average accuracy of 87.8% and a maximum accuracy of 95.0% for detecting the freshness level of a single fruit category. Experimental results confirm the effectiveness of our proposed method in accurately detecting and localizing the freshness level of fruits, demonstrating its potential for wide application and promotion in the fruit industry.

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References

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LIU Y G. Research on rapid recognition technology of apple freshness feature based on gas sensor array optimization [D]. North central university, 2021. DOI: 10.27470 /, dc nki. GHBGC. 2021.000587.

Xiao Pengcheng, Xu Wenguang, Zhang Yan, Zhu Liguang, Zhu Rong, Xu Yunfeng. Scrap classification rating method based on SE attention mechanism [J]. Journal of engineering science, 2023, (8) : 1342-1352. The DOI: 10.13374 / j.i ssn2095-9389.2022.06.10.002.

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Published

05-06-2023

Issue

Section

Articles

How to Cite

Wei, Z., Chang, M., & Zhong, Y. (2023). Fruit Freshness Detection Based on YOLOv8 and SE attention Mechanism. Academic Journal of Science and Technology, 6(1), 195-197. https://doi.org/10.54097/ajst.v6i1.9125