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ARS Home » Southeast Area » Booneville, Arkansas » Dale Bumpers Small Farms Research Center » Research » Publications at this Location » Publication #370552
Research Project: Sustainable Small Farm and Organic Grass and Forage Production Systems for Livestock and Agroforestry

Location: Dale Bumpers Small Farms Research Center

Title: Methods for identification and quantification of rock fragment concentrations and distribution in a natural soil profile

Author
item JIANG, ZHUODONG - Shenyang Agricultural University
item WANG, QUIBING - Shenyang Agricultural University
item ADHIKARI, KABINDRA - University Of Arkansas
item BRYE, KRISTOPHOR - University Of Arkansas
item SUN, ZHONGXIU - Shenyang Agricultural University
item SUN, FUJUN - Shenyang Agricultural University
item Owens, Phillip

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/10/2019
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: As an indispensable part of soil morphological characteristics, rock fragments (RFs), when present, play an important role in physical, chemical, and biological properties and processes in soils that can influence land use and management decisions. Rock fragment concentrations are difficult to quantify and most often are visually estimated in the field. The visual-estimate (VE) method performed with a chart by professional soil classifiers is common, but strongly depends on experience and practice. The direct measurement (DM) method is more accurate, but is costly and labor-intensive. Due to its importance, a simple and inexpensive method for estimating RF concentrations without destructive soil sampling is necessary. The development of camera technology and digital image processing provide an opportunity for soil descriptions and quantitative analyses using a photogrammetric method (PM). Therefore, the objective of this study was to explore the potential for RF concentration estimation using a PM. Apart from VE and DM, different image processing and a photogrammetric method were compared to propose an unbiased and efficient RF quantification method. Results showed that the hue-saturation-value (HSV) color model performed better than the red-green-blue (RGB) color model for identifying RFs, as the HSV model had larger intersection over union (IoU), sensitivity (SE), and specificity (SP) indices. The region-growing-watershed algorithm with pre-segmentation processes in the HSV model accurately segmented 84.9% of the RFs from the rest of soil matrix. The PM-estimated results were compared with the VE and DM methods. Results estimated by the DM method were considered as the true value to assess the accuracy and precision of the PM estimates. The PM had a greater correlation with DM (r = 0.81) compared to the VE method (r = 0.78). Results showed that the proposed PM could be used to accurately and efficiently estimate RF concentrations in a natural soil profile.