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