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

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.