Computer Vision for Particle Size Analysis of Coarse-Grained Soils

TL;DR

This paper introduces a cost-effective, mobile phone-based computer vision method for soil particle size analysis, offering rapid, on-site results that challenge traditional laboratory sieve techniques, especially for particles larger than 2 mm.

Contribution

The study presents a novel, accessible approach using standard mobile phones and open-source software for soil PSA, reducing reliance on labor-intensive traditional methods.

Findings

Achieves approximately 6% MAPE for particles >2mm

Higher error (~60%) for particles <2mm, suggesting resolution limits

Method enables immediate, on-site soil analysis without laboratory equipment

Abstract

Particle size analysis (PSA) is a fundamental technique for evaluating the physical characteristics of soils. However, traditional methods like sieving can be time-consuming and labor-intensive. In this study, we present a novel approach that utilizes computer vision (CV) and the Python programming language for PSA of coarse-grained soils, employing a standard mobile phone camera. By eliminating the need for a high-performance camera, our method offers convenience and cost savings. Our methodology involves using the OPENCV library to detect and measure soil particles in digital photographs taken under ordinary lighting conditions. For accurate particle size determination, a calibration target with known dimensions is placed on a plain paper alongside 20 different sand samples. The proposed method is compared with traditional sieve analysis and exhibits satisfactory performance for soil particles larger than 2 mm, with a mean absolute percent error (MAPE) of approximately 6%. However, particles smaller than 2 mm result in higher MAPE, reaching up to 60%. To address this limitation, we recommend using a higher-resolution camera to capture images of the smaller soil particles. Furthermore, we discuss the advantages, limitations, and potential future improvements of our method. Remarkably, the program can be executed on a mobile phone, providing immediate results without the need to send soil samples to a laboratory. This field-friendly feature makes our approach highly convenient for on-site usage, outside of a traditional laboratory setting. Ultimately, this novel method represents an initial disruption to the industry, enabling efficient particle size analysis of soil without the reliance on laboratory-based sieve analysis. KEYWORDS: Computer vision, Grain size, ARUCO