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Fruit Development, Structure, and Function

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Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.

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Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

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Image-Based Segregation of High-Quality Dragon Fruits Among Ripe Fruits.

Coral Ortiz¹, Nikita Dapurkar², Vicente Alegre³

¹Rural and Agri-Food Engineering Department, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This study introduces a non-destructive image analysis method for classifying dragon fruit quality. The system accurately sorts fruits by ripeness and weight, offering a cost-effective alternative to destructive testing.

Keywords:

dragon fruit firmness image analysis quality

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

Area of Science:

Agricultural Science
Computer Vision
Food Quality Assessment

Background:

Growing demand for high-quality dragon fruit in Europe necessitates efficient quality control.
Traditional destructive methods for assessing fruit quality are time-consuming and costly.

Purpose of the Study:

To develop and validate a non-destructive image analysis system for classifying dragon fruit quality.
To correlate image-derived parameters with fruit ripeness and weight for quality categorization.

Main Methods:

Utilized visible and ultraviolet lighting for image acquisition of dragon fruits.
Extracted non-destructive parameters (perimeter, diameter, area, RGB color) and correlated them with destructive measurements (weight, soluble solids, acidity).
Employed cluster and discriminant analysis to classify fruits into three quality categories.

Main Results:

Achieved an overall classification accuracy of nearly 85% for dragon fruits.
Successfully identified very high-quality (riper, larger) dragon fruits with 93% accuracy.
Validated the system's consistency and effectiveness on an independent sample set.

Conclusions:

Non-destructive image analysis is a viable, cost-effective method for dragon fruit quality assessment.
Integration of smart technologies in postharvest operations can enhance efficiency and product consistency.
The developed model supports automated quality sorting, reducing labor costs and improving industry standards.