Reflectance Multispectral Imaging for Soil Composition Estimation and USDA Texture Classification

TL;DR

This paper introduces a cost-effective multispectral imaging system combined with machine learning to accurately predict soil composition and classify USDA soil textures in the field, offering a rapid alternative to traditional lab tests.

Contribution

It presents a novel field-deployable MSI system and a machine learning framework that accurately estimates soil composition and classifies USDA soil textures using spectral data.

Findings

Achieved R^2 up to 0.99 for soil composition prediction.

Over 99% accuracy in USDA soil texture classification.

Demonstrated effective non-destructive soil analysis in field conditions.

Abstract

Soil texture is a foundational attribute that governs water availability and erosion in agriculture, as well as load bearing capacity, deformation response, and shrink-swell risk in geotechnical engineering. Yet texture is still typically determined by slow and labour intensive laboratory particle size tests, while many sensing alternatives are either costly or too coarse to support routine field scale deployment. This paper proposes a robust and field deployable multispectral imaging (MSI) system and machine learning framework for predicting soil composition and the United States Department of Agriculture (USDA) texture classes. The proposed system uses a cost effective in-house MSI device operating from 365 nm to 940 nm to capture thirteen spectral bands, which effectively capture the spectral properties of soil texture. Regression models use the captured spectral properties to estimate clay, silt, and sand percentages, while a direct classifier predicts one of the twelve USDA textural classes. Indirect classification is obtained by mapping the regressed compositions to texture classes via the USDA soil texture triangle. The framework is evaluated on mixture data by mixing clay, silt, and sand in varying proportions, using the USDA classification triangle as a basis. Experimental results show that the proposed approach achieves a coefficient of determination R^2 up to 0.99 for composition prediction and over 99% accuracy for texture classification. These findings indicate that MSI combined with data-driven modeling can provide accurate, non-destructive, and field deployable soil texture characterization suitable for geotechnical screening and precision agriculture.