Image-based and ML-driven analysis for assessing blueberry fruit quality.

ABSTRACT: The assessment of blueberry fruit quality is traditionally conducted through laboratory equipment. Despite its high accuracy, this method remains destructive, labor-intensive, time-consuming, and costly. Consequently, there is a pressing need for innovative solutions such as image-based and artificial intelligence (AI)-driven analysis. To address these limitations, this study aimed to analyze whether an approach based on mobile image-based analysis combined with machine learning (ML) algorithms could develop a non-destructive framework for evaluating blueberry fruit quality, specifically focusing on total soluble solids (TSS) and firmness. Firstly, we collected numerous blueberry samples during the maturity stage to construct a comprehensive dataset. These samples were meticulously analyze

SUBMITTER: Barbosa Junior MR 

PROVIDER: S-EPMC11834081 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature