Optical granulometry

Short description: Photographic measurement of the distribution of grain sizes in a material

Granulometry

Basic concepts
Particle size * Grain size
Size distribution * Morphology
Methods and techniques
Mesh scale * Optical granulometry
Sieve analysis * Soil gradation

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Related concepts
Granulation * Granular material
Mineral dust * Pattern recognition
Dynamic light scattering

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Optical granulometry is the process of measuring the different grain sizes in a granular material,[1] based on a photograph. Technology has been created to analyze a photograph and create statistics based on what the picture portrays. This information is vital in maintaining machinery in various trades worldwide. Mining companies can use optical granulometry to analyze inactive or moving rock to quantify the size of these fragments.[2] Forestry companies can zero in on wood chip sizes without stopping the production process, and minimize sizing errors.[3]

In order for optical granulometry to be completely successful, an accurate photo must be taken – under sufficient lighting, and using proper[clarification needed] technology – to obtain quantified results.[4] If these requirements are met, an image analysis system can be implemented.

## Contents

* 1 The process
* 2 Smartphone-based, segmentation-free estimation of grain size distribution
* 3 See also
* 4 Notes
* 5 References
* 6 External links

## The process

Software uses four basic steps in determining the average size of material:

Step 1: Taking a photo- A sample of the material you would like to measure Step 2: Edge Detection- By completing an edge detection process, the software can determine the various sizes of material in the photo Step 3: Virtual Sieve- The length to width ratio is measured in this step of the process. The individual particle sizes are measured instantly. Step 4: Graphical Output- In this step, statistical data and size distribution is plotted on an easy-to-read form for operators.

See the Wikipedia article on Photoanalysis to see how mining, forestry and agricultural companies are using this technology to improve quality control techniques.

## Smartphone-based, segmentation-free estimation of grain size distribution

Recently, a methodology has emerged by which soil grain size distribution can be inferred from optical images acquired with commodity smartphones by training convolutional neural networks to predict parameters of the distribution curve directly from the image, without explicit image segmentation [5]. In this approach, a standardized image of a soil surface is captured under controlled conditions, preprocessed to reduce device-specific variability, and passed to a regression model that outputs the parameters of a cumulative distribution function e.g., a two-parameter Weibull curve. The resulting distribution can be used to derive geotechnical descriptors and class boundaries.

## See also

* Particle-size distribution
* Grain size
* Granulometry (morphology)

## Notes

1. ↑ Definition of granularity at GEMET
2. ↑ (Franklin and Katsabanis 1996), page 68
3. ↑ (Franklin and Katsabanis 1996), page 47
4. ↑ (Franklin and Katsabanis 1996), page 115
5. ↑ (Soranzo 2025)

## References

* Measurement of Blast Fragmentation: Proceedings of a Workshop Held Parallel With Fragblast-5, Montreal, 26–29 August 1996, by John A. Franklin, Takis Katsabanis, Published by Taylor & Francis, 1996, ISBN 90-5410-845-2
* Convolutional neural network prediction of the particle size distribution of soil from close-range images, Enrico Soranzo, Carlotta Guardiani, Wei Wu, "Soils and Foundations" 65(1) (2025), doi:10.1016/j.sandf.2025.101575, DOI

## External links

* Free Web Application for Particle and Grain Analysis

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