Non-Destructive Techniques for the Analysis and Evaluation of Strawberry Quality: A Review

Strawberry is a high-value horticultural crop whose soft and perishable nature makes rapid and reliable quality evaluation essential throughout production, storage, and postharvest handling. Conventional sensory and destructive physicochemical analyses are often unsuitable for high-throughput and automated applications, driving increasing interest in nondestructive quality assessment and prediction technologies. This review summarizes recent advances in four major approaches: vision-based imaging, spectroscopy-based detection, sensor-based detection, and crop model-based prediction. Vision-based methods have evolved from handcrafted image features and conventional machine learning toward deep learning, object detection, instance segmentation, and three-dimensional analysis for evaluating maturity, color, morphology, defects, and diseases. Spectroscopic techniques, particularly near-infrared spectroscopy and hyperspectral imaging, enable nondestructive estimation of internal quality attributes such as soluble solids content, firmness, acidity, and moisture. Sensor-based approaches, especially electronic noses, characterize changes in volatile organic compounds to assess freshness, maturity, mechanical damage, and storage-related deterioration. Crop models, exemplified by DSSAT CSM-CROPGRO-Strawberry, provide a process-based framework for predicting fruit development and quality dynamics in response to environmental conditions. Collectively, advances in machine learning, deep learning, sensing technologies, and process-based modeling are shifting strawberry quality evaluation from static measurement toward automated and dynamic prediction. Future research should prioritize model robustness and cross-cultivar generalizability, lightweight algorithms, multimodal data fusion, integration of crop models with nondestructive sensing, and online detection systems to facilitate practical deployment in precision production and postharvest management.