Non‐Destructive Prediction Approach for Pomelo Granulation Rate Using <scp>MRI</scp> Technology

ABSTRACT Pomelo is prone to juice sac granulation during ripening and storage, resulting in decreased moisture content, hard flesh, and bland flavor, which affects market value and consumer experience. Due to the complexity of the causes of juice sac granulation, coupled with the thick peel of pomelo, the detection means of pomelo is lacking and difficult to detect. In order to solve the shortcomings of the traditional granulation rate calculation, which has an error and cannot realize non‐destructive detection. This study proposes a non‐destructive detection of pomelo juice sac granulation and the calculation of granulation rate based on the nuclear magnetic resonance imaging (MRI) technology combined with the computer vision technology. Based on the strong analytical power of the MRI technology for moisture and the change of moisture in the granulated juice sac of pomelo, in this study, the cross section of pomelo was scanned by MRI scanner to obtain the corresponding MRI images. The MRI images of the cross section of pomelo were segmented using the threshold segmentation method to realize the calculation of the percentage of granulated pulp. The experimental results show that the segmentation threshold T 1 = 102 can effectively segment the pulp from the peel, and the segmentation threshold T 2 = 175 can effectively segment the granulated pulp from the normal pulp. The granulation rate of pomelo pulp can be calculated by calculating the ratio of the granulated pulp area to the total pulp area. Therefore, based on the MRI technology can be used for the detection of the granulation rate of pomelo juice sac, which provides a certain reference value for the subsequent non‐destructive detection of the internal quality of large melons and fruits.