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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Image-based and ML-driven analysis for assessing blueberry fruit quality.

Marcelo Rodrigues Barbosa Júnior¹, Regimar Garcia Dos Santos¹, Lucas de Azevedo Sales¹

¹Department of Horticulture, University of Georgia, Tifton, GA, 31793, USA.

|February 19, 2025

Summary

This study developed a non-destructive method using mobile images and machine learning (ML) to assess blueberry quality, specifically total soluble solids (TSS) and firmness. This approach offers a cost-effective and efficient alternative to traditional lab testing for improved fruit harvesting.

Keywords:

Artificial intelligence Fruit firmness Precision horticulture Predictive models RGB images Sugar content

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Area of Science:

Horticulture
Computer Science
Agricultural Engineering

Background:

Traditional blueberry quality assessment is destructive, labor-intensive, and costly.
There is a need for non-destructive, efficient, and cost-effective methods.
Image-based and AI-driven analysis offer promising alternatives.

Purpose of the Study:

To develop a non-destructive framework for blueberry fruit quality evaluation using mobile image analysis and machine learning (ML).
To predict total soluble solids (TSS) and firmness non-destructively.
To assess the feasibility of using mobile RGB images for quality assessment.

Main Methods:

Collected blueberry samples at maturity, measuring diameter, TSS, firmness, and color in the lab.
Captured RGB images of blueberries using a mobile device.
Processed images to extract spectral bands and applied eight ML algorithms to build predictive models.

Main Results:

Initial correlation analysis showed RGB images had a suggestive contribution (r < 0.41).
ML integration significantly improved predictive accuracy (R² = 0.71–0.99, MAE = 0.003–0.28, RMSE = 0.004–0.31).
The developed models demonstrated high accuracy in predicting blueberry quality parameters.

Conclusions:

Mobile image-based analysis combined with ML provides a non-destructive, cost-effective, and efficient method for blueberry quality assessment.
This approach supports high-quality blueberry harvesting and advancements in precision agriculture.
The findings confirm the applicability of mobile imaging for practical quality control in the fruit industry.