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3D Quantitative Modeling for Stone Fruit Quality Assessment by LF-NMRI.

Kang Wang¹, Bing Li¹, Shan Zeng¹

¹School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China.

Foods (Basel, Switzerland)

|June 12, 2026

Summary

This study introduces a new 3D modeling method for stone fruits using low-field nuclear magnetic resonance imaging (LF-NMRI). The technique accurately quantifies core volume ratio (CVR) non-destructively, improving fruit quality analysis.

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Area of Science:

Agricultural Engineering
Biomedical Engineering
Computer Vision

Background:

Core Volume Ratio (CVR) is crucial for assessing edible fruit fractions.
Traditional CVR measurement involves destructive sampling.
Low-field Nuclear Magnetic Resonance Imaging (LF-NMRI) offers non-destructive analysis but faces spatial resolution limitations.

Purpose of the Study:

To develop a high-precision 3D modeling method for non-destructive internal quality analysis of stone fruits.
To overcome the spatial quantification limitations of LF-NMRI in fruit analysis.
To accurately determine the Core Volume Ratio (CVR) using a novel 3D reconstruction approach.

Main Methods:

Acquisition of tomographic LF-NMRI sequences along three orthogonal axes.

Keywords:

CVR LF-NMRI multi-view 3D reconstruction non-destructive assessment

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Isolation and Biophysical Study of Fruit Cuticles

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Segmentation of fruit pulp and core regions using a SwinUNet deep learning model.

Registration of point clouds from three views using a genetic algorithm and fusion into a unified 3D model via Poisson surface reconstruction.

Main Results:

Achieved a highly accurate CVR estimation with a mean absolute error of 0.13% compared to manual measurements.
The three-view reconstruction strategy resulted in a volumetric error of only 0.73%.
Significantly outperformed single-view (4.57%) and dual-view (3.73%) reconstruction approaches.

Conclusions:

The proposed method provides a robust and accurate non-destructive solution for 3D internal fruit quality analysis.
This technology enables precise quantification of core and entire fruit volumes.
The SwinUNet and multi-view reconstruction approach enhance the capabilities of LF-NMRI for agricultural applications.