Image-Based Interpolation of Soil Surface Imagery for Estimating Soil Water Content

Eunji Jung, Dongseok Kim, Jisu Song and Jaesung Park ()
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Eunji Jung: Department of Bio-Industrial Machinery Engineering, Pusan National University, Miryang 50463, Republic of Korea
Dongseok Kim: Department of Bio-Industrial Machinery Engineering, Pusan National University, Miryang 50463, Republic of Korea
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, 2025, vol. 15, issue 17, 1-24

Abstract: Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce.

Keywords: data interpolation; HSV; image processing; RGB; soil water content (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: 2025
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