Non-Destructive Assessment of Fruit Maturity and Ripeness Using Advanced Sensing Technologies and Machine Learning

Fruit maturity and ripeness determine harvest timing, postharvest management, market quality, and consumer acceptance. Conventional assessments commonly depend on destructive physicochemical analyses or subjective visual inspection, which limits rapid and repeated evaluation across production and supply chains. This review synthesizes recent advances in non-destructive fruit maturity and ripeness assessment using machine vision, visible and near-infrared spectroscopy, hyperspectral and multispectral imaging, acoustic and mechanical techniques, electronic noses, and dielectric measurements. Particular attention is given to data preprocessing, feature extraction, chemometric modeling, conventional machine learning, and deep learning approaches used to associate sensor responses with firmness, soluble solids content, acidity, pigment composition, internal defects, and physiological stage. The reviewed evidence indicates that spectroscopy and imaging provide strong predictive performance, while multimodal sensing can improve reliability by combining complementary external and internal attributes. However, differences in cultivar, orchard conditions, instruments, sampling protocols, reference measurements, and validation strategies restrict model transferability and cross-study comparison. The review further identifies the gaps that most constrain deployment, which include the absence of standardized maturity definitions, the scarcity of multisite datasets with external validation, the limited reporting of explainable models and uncertainty estimates, and the few sensor-fusion systems tested under edge-deployment conditions.