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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Published on: August 23, 2017

Image processing based modeling for Rosa roxburghii fruits mass and volume estimation.

Zhiping Xie¹, Junhao Wang², Yufei Yang²

¹School of Mechanical & Electrical Engineering, Guizhou Normal University, Guiyang, China. xzpfeiniao@163.com.

Scientific Reports

|July 5, 2024

Summary

Image analysis accurately estimates Rosa roxburghii fruit mass and volume using regression models. This automated approach aids fruit grading, offering a faster alternative to manual methods.

Keywords:

Rosa roxburghii Estimated modeling Grading Image measurement Physical characteristic

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Area of Science:

Agricultural Engineering
Horticulture
Image Processing

Background:

Fruit grading and consumer selection rely on accurate mass and volume measurements.
Manual grading is labor-intensive and time-consuming.
Image-based estimation offers an automated solution for fruit characterization.

Purpose of the Study:

To develop and evaluate image processing and regression models for estimating the mass and volume of Rosa roxburghii fruits.
To compare the performance of univariate and multivariate regression models for predicting fruit physical characteristics.

Main Methods:

Utilized image processing techniques to extract fruit dimensions and projected areas.
Applied univariate (linear, quadratic, exponential, power) and multivariate regression models.
Validated model accuracy using R-squared values and prediction accuracy percentages.

Main Results:

A quadratic regression model using criterion projected area (CPA) achieved the highest mass estimation accuracy (99.27%, R²=0.981).
A multivariate regression model incorporating three projected areas (PA1, PA2, PA3) yielded the best volume estimation (98.24%, R²=0.898).

Conclusions:

Automated image-based methods provide accurate estimations of Rosa roxburghii fruit mass and volume.
Regression models, particularly quadratic for mass and multivariate for volume, are effective tools for this application.
Simplified approaches using single-view dimensional or projected area data are suitable for lower accuracy requirements, enabling cost savings in practical grading systems.