Quantifying Soil Particle Settlement Characteristics through Machine Vision Analysis Utilizing an RGB Camera

Donggeun Kim, Jisu Song and Jaesung Park ()
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Donggeun Kim: Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
Jisu Song: Department of Bio-Industrial Machinery Engineering, Pusan National University, Miryang 50463, Republic of Korea
Jaesung Park: Department of Bio-Industrial Machinery Engineering, Pusan National University, Miryang 50463, Republic of Korea

Agriculture, 2023, vol. 13, issue 9, 1-17

Abstract: Soil particle size distribution is a crucial factor in determining soil properties and classifying soil types. Traditional methods, such as hydrometer tests, have limitations in terms of time required, labor, and operator dependency. In this paper, we propose a novel approach to quantify soil particle size analysis using machine vision analysis with an RGB camera. The method aims to overcome the limitations of traditional techniques by providing an efficient and automated analysis of fine-grained soils. It utilizes a digital camera to capture the settling properties of soil particles, eliminating the need for a hydrometer. Experimental results demonstrate the effectiveness of the machine vision-based approach in accurately determining soil particle size distribution. The comparison between the proposed method and traditional hydrometer tests reveals strong agreement, with an average deviation of only 2.3% in particle size measurements. This validates the reliability and accuracy of the machine vision-based approach. The proposed machine vision-based analysis offers a promising alternative to traditional techniques for assessing soil particle size distribution. The experimental results highlight its potential to revolutionize soil particle size analysis, providing precise, efficient, and cost-effective analysis for fine-grained soils.

Keywords: soil particle size analysis; machine vision; RGB camera; settling characteristic; image analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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