A novel stone fruit quality non-destructive evaluation method based on PVR by LF-NMRI

For stone fruits, the pulp ratio, indicating the proportion of pulp to the total fruit, serves as a vital metric in assessing stone fruit quality. Traditionally, this ratio is computed by measuring the mass of each component through destructive sampling, and acquired results remain controversial. In order to obtain this ratio non-destructively and more accurately, a novel evaluation method for stone fruit quality based on pulp volume ratio (PVR) by low-field nuclear magnetic resonance imaging (LF-NMRI) is proposed. This approach integrates the SwinUnet segmentation network to differentiating the fruit pulp and core structure, thereby precisely computing the volume proportion of pulp. Experimental results reveal that SwinUnet exhibits better accuracy and robustness in the LF-NMRI data compared to the other networks. Based on the segmentation results, the disparities between the PVR values computed using the ellipse fitting method and the integral method, compared to the ground truth (GT), are less than 1% and 0.5% respectively. This paper provides a new reference for selecting premium stone fruits and enriches the fruit quality evaluation system.