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Purpose <p>Precise segmentation of blueberry maturity is critical for optimizing harvestschedules and maintaining product quality. Traditional methods, which rely on manualinspection, are not only labor-intensive but also cost-inefficient. This study presents a novelframework that integrates deep learning-based super-resolution reconstruction (SRR) withsemantic segmentation to provide a fast and accurate solution for maturity assessment.</p> Methods <p>The SRR module enhances image resolution, enabling more detailed feature extraction.Semantic segmentation models—incorporating convolutional neural networks (CNNs),Transformer-based models, and the Mamba-based state space architecture—further improvesegmentation precision.</p> Results <p>Experimental results indicate that the MambaIR modelachieves a structural similarity index measure (SSIM) of 82.26% in SRR tasks, while the Mamba-based segmentation model attains a mean Intersection over Union (mIoU) of 83.15%.</p> Conclusion <p>By uniting SRR and semantic segmentation, our framework not only advances thetechnical accuracy of maturity detection but also holds strong potential for real-time, cost-effective deployment in precision agriculture systems, supporting intelligent decision-making at scale.</p>

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Smart UAV-assisted blueberry maturity monitoring with Mamba-based computer vision

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月3日

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  • Fan Zhao,
  • Yinyin He,
  • Jian Song,
  • Jiaqi Wang,
  • Dianhan Xi,
  • Xinlei Shao,
  • Qingyang Wu,
  • Yongying Liu,
  • Yijia Chen,
  • Guochen Zhang,
  • Chenyu Zhang,
  • Yulun Chen,
  • Jundong Chen,
  • Katsunori Mizuno

摘要

Purpose

Precise segmentation of blueberry maturity is critical for optimizing harvestschedules and maintaining product quality. Traditional methods, which rely on manualinspection, are not only labor-intensive but also cost-inefficient. This study presents a novelframework that integrates deep learning-based super-resolution reconstruction (SRR) withsemantic segmentation to provide a fast and accurate solution for maturity assessment.

Methods

The SRR module enhances image resolution, enabling more detailed feature extraction.Semantic segmentation models—incorporating convolutional neural networks (CNNs),Transformer-based models, and the Mamba-based state space architecture—further improvesegmentation precision.

Results

Experimental results indicate that the MambaIR modelachieves a structural similarity index measure (SSIM) of 82.26% in SRR tasks, while the Mamba-based segmentation model attains a mean Intersection over Union (mIoU) of 83.15%.

Conclusion

By uniting SRR and semantic segmentation, our framework not only advances thetechnical accuracy of maturity detection but also holds strong potential for real-time, cost-effective deployment in precision agriculture systems, supporting intelligent decision-making at scale.